Personalization for E-Commerce

Personalization for E-Commerce Tutorial presented at UM 2001

Anthony Jameson German Research Center for Artificial Intelligence (DFKI) Saarbrücken, Germany [email protected] http://dfki.de/~jameson

Table of Contents

3

Introduction What Is Personalization in General? Types of Systems Covered Emphasis in This Tutorial Availability of These Slides

Recommending Products PersonaLogic PurchaseSource The FindMe Systems Casper Lifestyle Finder Amazon.com Recommendations Comparison

Facilitating Navigation Clixsmart Navigator Adaptive Information Server Adaptive Web Sites Angara Converter 7d b-bot Comparison

Allowing Configuration MyYahoo! InfoBeans Comparison

Using Lifelike Characters The Notebook Expert Artificial Life The PPP Persona The MIAU Agents REA Comparison

5 5 6 7 8

9 9 12 24 34 38 41 45

53 53 55 62 67 75 84

87 87 99 107

109 109 120 130 137 146 152

5

Introduction

6

Introduction 5

What Is Personalization in General?

Adaptability

Adaptation

Anthropomorphism

Personalization can be seen as the union of three overlapping system properties

Types of Systems Covered

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1. Product recommendation systems 2. Systems that guide users to the appropriate pages in a hypertext system 3. Systems that users can configure to suit their own requirements and preferences 4. Lifelike characters

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Introduction

8

7

Emphasis in This Tutorial Technology for personalization

Personalized systems

Enhanced user experience

Increases in: Customer acquisition Cross−selling Up−selling Customer loyalty ...

Availability of These Slides

8

• Several companies supplied information for this tutorial on the

condition that it would be made available only to the tutorial participants • Accordingly, an electronic version of these slides cannot be made

available via the World-Wide Web • For the same reason, the printed slides may be photocopied only on

a small scale (e.g., for individual colleagues) • Those who require electronic versions of particular parts of the

slides can contact the presenter or the companies in question

9

Recommending Products / PersonaLogic

10

Recommending Products http://www.personalogic.com. As of July, 2001, the system is no longer available at this URL. Specific parts of it are available − for example, at http://www.purina.personalogic.com, from which the following examples are taken.

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Example Screens (1)

At PersonaLogic, U can obtain recommendations about a broad range of "products" (including colleges, dogs, and political candidates) To get recommendations concerning dogs, U first answers questions about her evaluation criteria

Example Screens (2)

Whenever U visits the "YOUR RESULTS" page, S presents the products that score best according to the evaluation criteria that U has specified

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Recommending Products / PersonaLogic

Multi-Attribute Utility Theory (MAUT)

11 For general treatments of MAUT, see von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge, England: Cambridge University Press.; Clemen, R. T. (1996). Making hard decisions: An introduction to decision analysis. Pacific Grove, CA: Duxbury.

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Role in personalized product presentation • Many (though not all) systems for personalized product presentation

use some variant of MAUT • The particular way in which it is used can vary greatly

Basic principles • Each object O has a number of attributes that are relevant to its evaluation by U • Each attribute has an importance weight for U • U could in principle assign to each attribute of O a value (e.g., on a scale from 0 to 10)

O to U is the sum of the values of its attributes, weighted by the importances of the attributes

• The overall value of • What •

U wants is the object O with the highest overall value

Note: "Price" is usually viewed as an attribute, usually one with a high importance weight

PurchaseSource http://www.frictionless.com/. As of July, 2001, the demo that was used to make the following slides is no longer available.

Introduction • Frictionless.com created a web-based system (S) called

PurchaseSource • On-line vendors can use this system to help customers locate

suitable products • What we will see on the following slides is a walkthrough of a

demonstration of this system • What is of interest is the basic system itself, not the specific

information that has been entered on the particular type of product that our hypothetical U is looking for (backpacks)

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Recommending Products / PurchaseSource

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Overview of Types of Sports Articles

•

U has already indicated that he is interested in sports articles

•

U now clicks on "Backpacks" under "Outdoors"

Choosing a "Profile"

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• By choosing the

profile "Day Hiker",

U allows S to make guesses about many of U’s preferences •

U will now need to specify only the preferences that S has not guessed correctly

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Specifying Preferences • For each attribute,

U can

specify how important it is that the product should fall into the specified range •

U can learn more about the attribute by clicking the "learn more" button •

•

For the attribute "Capacity", see the result on the slide after next

U can also "edit" the range itself •

Here, our U chooses to edit the range for "Capacity" by clicking on the first "Edit" button

Editing the Range for an Attribute

• Before editing the

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range, our U chooses to "learn more" about the attribute "Capacity"

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Learning More About an Attribute

• After reading this

information, our U goes "back" to the previous screen

U uses the right-hand pop-up menu to change the maximum capacity to 2,500 cu. in

• There,

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Overview of the Recommended Products (1)

• Each recommended

product is summarized here in a single line

Overview of the Recommended Products (2)

• Our

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U clicks on the

light-colored bar for the last product listed ("Jansport Couloir") to find out more about the reasons for the recommendation

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Summary for a Single Product

• This page refers to a

single product, but it still doesn’t include all of the detailed considerations underlying S’s rating of this product

U wants to know these details, so he clicks on "Details"

• Our

Details for a Single Product

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U can see, for each attribute, how S

• Here,

rated this product with respect to this attribute • (Only the first part of the

long screen is shown here)

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Comparison of Three Products • Later, our

U chooses to request

a comparison of three other backpacks, which were more highly rated • In each column of this screen,

essentially the same information is presented as in the screen with details for a single product (see the previous slide)

The FindMe Systems Burke, R. D., Hammond, K. J., & Young, B. C. (1997). The FindMe approach to assisted browsing. IEEE Expert, 12(4), 32−40. Burke, R. D. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Science. http://www.ics.uci.edu/~burke/research/cbr-ec.html

Introduction

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The FindMe family • Burke and his colleagues developed several related systems that

are based on the "Find-Me" approach to recommendation • The basic principles are illustrated here with the restaurant recommendation system "Entree", which is available on the web

Overview of Entree Specification of preferences • At the beginning of a session,

U has two options:

1. U specifies her preferences by choosing a few properties of restaurants 2. U enters the name of some other restaurant that she knows (in Chicago or another city) which exemplifies her preferences

Criticism of the initial results • S presents some restaurants that correspond to

U’s specifications

• U can then specify how the set of candidates can be improved

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Recommending Products / The FindMe Systems

The Entree-System is available under http://infolab.ils.nwu.edu/entree/pub/

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Entree: Examples (1)

U specifies her interests


S offers an initial result, further results, and an opportunity to criticize the results

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U would prefer a similar restaurant with South American cuisine


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Faced with this result, U clicks on the button "Creative" in order to find a similar but more creative restaurant

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By now the set of candidates has been reduced to a single restaurant

Key Methods (1)

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This example illustrates some of the methods that are applied in the FindMe approach:

Retrieval by similarity 1. At first, U specifies either • An example product; or • A small set of attributes that correspond to various goals •

Examples: "cuisine" = "French", "style" = "casual"

2. S orders all of the products in the database according to their similarity with the given example or attribute set • For each goal, a similarity metric has to be defined • For example, S needs to know how similar French and Japanese cuisine are • Defining these similarity metrics is one of the main tasks that has to be handled when a system like Entree is introduced in a new domain

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Key Methods (2)

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Different orderings of goals • When restaurants are sorted, the various goals are taken into

account in a particular order • Example: •

First they are sorted in terms of the similarity of their cuisine to

U’s specification •

Then restaurants that are equally similar with respect to cuisine are sorted with respect to "atmosphere"

•

Assumption here: cuisine is a more important criterion than atmosphere

• Handling differences among users in the importance of goals •

•

In some of the FindMe systems, U can specify the relative importance of the goals herself She can choose from several prespecified "retrieval strategies" (e.g., "Money is no object")

Key Methods (3)

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Criticizing (tweaking) of the candidate restaurants by U U, S simply removes from the candidate set all restaurants that do not satisfy the specified constraint

• After a critique by

•

Example: All restaurant that are not "more creative" than the most recently suggested one

Use of familiar concepts •

S makes it possible for U to employ concepts that are more familiar and less concrete than the attributes stored in the database (e.g., casual)

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Key Methods (4)

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Explaining conflicts among specifications •

U may sometimes specify a combination of desired attributes that (almost) never occurs •

Example from the automobile domain: powerful motor combined with low gas consumption

• In this case •

S calls attention to the conflict

Note: This functionality is not realized in Entree, but it is realized in the related system Car Navigator (Burke et al., 1997)

Casper Bradley, K., Rafter, R., & Smyth, B. (2000). Case-based user profiling for content personalisation. In P. Brusilovsky, O. Stock, & C. Strapparava (Eds.), Adaptive hypermedia and adaptive web-based systems: Proceedings of AH 2000 (pp. 62−72).Berlin: Springer.

Case-Based Reasoning (1)

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Casper: Personalization in a job finding system • Underlying retrieval system: JobFinder, an Irish recruitment web site • JobFinder provides conventional search-engine functionality over a

database of job offers • Limitation: •

U’s search query seldom reflects all of her actual criteria

U ratings of jobs previously retrieved by JobFinder represent an additional source of information on U’s needs

• These previous "cases" are used to filter further cases retrieved by

the system

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Figure 1 of Bradley et al. (2000)

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Recommending Products / Casper


Overview of the two-stage retrieval process in Casper



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S’s criteria for assessing the similarity between two cases takes into account the domain-specific significance of the various attributes

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Recommending Products / Casper

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B

G G

G B B

? B

G

G B

B Bad

G

G Good Jobs ? Candidate Job


Procedure • The relevance of a new job offer is predicted on the basis of the

ratings of the k (here: 5) most similar previous cases ("nearest neighbors") • Each of these previous ratings is weighted by the similarity of the case to the current job offer

Evaluation results (summary) • About 70% correct classifications, using for each

57 job offers previously rated by that U

U a case base of

Lifestyle Finder The Lifestyle Finder was freely available on the web long enough to collect data from 20,000 users, but it is no longer available. Further information is given in the section "Inference: Data-Based". This screen shot is Figure 3 of: Krulwich, B. (1997). Lifestyle Finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18(2), 37−45.

Elicitation of Demographic Information

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The Lifestyle Finder elicits demographic information in a playful fashion that does not require U to supply identifying information 93% of the users surveyed agreed that the Lifestyle Finder’s questions did not invade their privacy

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The system recommends 15 web pages, of which 3 are chosen at random for evaluation purposes. This slide shows part of Figure 4 of: Krulwich, B. (1997). Lifestyle Finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18(2), 37−45.

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Recommending Products / Lifestyle Finder

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Demographically Based Recommendation (1)

After U has answered a few lifestyle-related questions (see the section "Properties"), the Lifestyle Finder’s presents its guess about the demographic cluster to which U belongs It then recommends web pages with information about products that should be of interest to persons within this cluster

Demographically Based Recommendation (2)

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Procedure of "demographic generalization" • The Lifestyle Finder assigns U to one or more of 62 demographic clusters from a commercial demographic system employed for consumer marketing • If U’s answers match more than one cluster, the predictions that are common to all matched clusters form the basis for recommendations for U • S may ask further questions that efficiently narrow down the set of possible clusters for U

Evaluation • Users gave positive responses to more than 40% of the web pages

recommended in this way, compared with about 30% of the randomly recommended pages • This level of accuracy is lower than that attainable through other methods • This method is accordingly proposed as a complement to methods that require more input from each user

41 Recommending Products / Amazon.com Recommendations 42

Amazon.com Recommendations Introduction

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• Amazon.com is a pioneer in the area of commercial product

recommendation • The recommendations are viewed as an additional service which is

intended to stimulate further purchases once U has purchased one or more books

• As we will see, the techniques employed are less well suited for

cases in which a customer who has specific requirements asks for recommendations

https://www.amazon.com/

Examples (1)

A link to the recommendation page is present in the introductory page

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43 Recommending Products / Amazon.com Recommendations 44

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Examples (2)

U sees this page once she has clicked on "See more Books recommendations"

Examples (3)

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On this page, U can influence the recommendations by explicitly rating the products she has bought so far (here: only 1 product)

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Recommending Products / Comparison

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Comparison 45

PersonaLogic

Comparison of Approaches (1) PurchaseSource

FindMe

Casper

Lifestyle Finder Amazon.com

1. What sort of explicit input does U have to supply?

Importances of General buyer attributes category; Importances and desired levels of attributes

Example or Normal queries; features of Ratings of desired product; offers Brief critiques of suggested products

Answers to None; or ratings amusing of products questions about demographic characteristics

2. What is the minimum information that U must supply before receiving her first tailored recommendation?

Importances for General buyer category one or more attributes

Normal query Example or features of desired product

Answers to the Purchase or questions rating of at least one product

Comparison of Approaches (2) PersonaLogic

PurchaseSource

FindMe

Casper

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3. To what extent (and how) does S ensure that U has enough general knowledge about the type of product in question to be able to make a well-founded decision?

Explanatory texts for individual attributes

Explanatory texts for individual attributes

Use of familiar concepts; sometimes explanation of conflicts

Not at all

Not at all

Some informative texts on site

4. How expressive are the means that U has available for communicating her requirements to S?

Very low Indirect and Extremely high Limited Quite high expressiveness, expressiveness, expressiveness expressiveness coarse within MAUT within MAUT framework framework

Low expressiveness

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PersonaLogic


48


FindMe

Casper


5. What properties of U are modeled?

Importance weights of attributes

Importance weights and desired values of attributes

Desired properties of product; sometimes relative importance of attributes

Membership in No modeling of Ratings of previous offers a demographic user properties group (no explicit modeling)

6. What procedure does S use to arrive at recommendations?

MAUT

MAUT

Case-based Mainly computation of reasoning similarities

Demographic marketing research conducted beforehand

Collaborative filtering


PurchaseSource

FindMe

Casper

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7. In what form does S give its recommendations?

Presentations Overall evaluation and at different levels of detail description

Brief textual description

Ordering of offers

Briefly annotated links to external web pages

Brief description, perhaps supplemented with reviews

8. To what extent can U see that a recommended product is better for her than particular other products?

Tabular comparisons

Little support Tabular comparisons at different levels of detail

Little support

Little support

Little support

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49

PersonaLogic


50


FindMe

Casper


9. Does S adapt the way in which it presents individual products to U?

No

Presentation refers to U’s requirements

No

No

No

No

10. To what extent can U revise the specification of her requirements after seeing some recommendations?

Possible but not Possible but not Specifically well supported well supported supported

Through Virtually revised queries impossible

To limited extent


PurchaseSource

FindMe

Casper

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11. To what extent does the recommendation process correspond with U’s natural way of thinking?

Well

Well

Well

Well

Poorly

Well

12. To what extent can U understand the reasons why S made particular recommendations?

Little support

Good support

Little support

No support

No support

No support

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51

PersonaLogic


52


FindMe

Casper


13. To what extent does S take into account the fact that the interests of U can vary from one situation and time to the next?

Not at all Problem cannot Problem cannot Problem cannot Most recent arise arise arise cases could be given greater weight

Only gradual adaptation possible

14. To what extent can S reuse what it has learned about U in connection with a particular recommendation process later, when making recommendations to U in another situation?

Currently not supported, in principle possible



Well

Well

Very well, for products of the same type


PurchaseSource

FindMe

Casper


15. When S is set up for use with a new type of product, to what extent do the designers have to make use of knowledge about that type of product?

A great deal

A great deal

A great deal

A good deal

To some extent Hardly any

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Facilitating Navigation / Clixsmart Navigator

54

Facilitating Navigation Smyth, B. (2001). Clixsmart navigator: A briefing for mobile and wireless portal operators. Dublin: ChangingWorlds. White paper.

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Reducing Click Distances: Before

To access the movie listings of her local theater via a WAP portal, the U in this example must make 29 clicks, often scrolling in the process

Reducing Click Distances: After

After S has adapted the menu structure to U’s access patterns, only 2 clicks are required

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Facilitating Navigation / Adaptive Information Server

56

Adaptive Information Server Background

The demos shown during the tutorial are available from the company’s web site: http://www.adaptiveinfo.com.

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• The firm AdaptiveInfo was founded recently as a spin-off from the

University of California at Irvine • It’s technology is based largely on research by Daniel Billsus and

Michael Pazzani • Representative articles: •

Billsus, D., & Pazzani, M. J. (1999). A hybrid user model for news story classification. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 99−108).Vienna: Springer Wien New York. http://www.ics.uci.edu/~pazzani/Publications/Publications.html

•

Billsus, D., & Pazzani, M. J. (2000). User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10, 147−180.

The slides in this section were adapted from slides supplied by Michael J. Pazzani.

Adaptive Information Server: Overview • Automatically learns a profile of user interests through normal

interaction • Prioritizes presentation of content and commerce opportunities • Proven to increase page views by over 40% • Designed for scalable data mining •

Real time: personalization

•

Aggregated for marketing insight

• Integrates with existing wireless platforms

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57


Adaptive News Server

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Some of the technology underlying the Adaptive Information Server is described by: Billsus, D., & Pazzani, M. J. (2000). User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10, 147−180.

58

A

B

C

The Adaptive News Server spontaneously adapts its selection of news items on the basis of the user’s reading behavior

Adaptive Classified Server

58

The good news: • Individual classified ads fit on most cell

phone screens

The bad news: • There are 250,000 ads in a paper, but

users won´t scan through 25 or press 25 keys to describe what they want • A newspaper costs $0.25 • The classified ad server must provide

more benefits than a newspaper

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Before Personalization

After Personalization

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Extra Functions With Phones

Adaptive Web Sites Perkowitz, M., & Etzioni, O. (2000). Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, 118, 245−275.

Introduction and Overview (1)

62

Background • The "adaptive web sites" approach is being developed by Mike

Perkowitz and Oren Etzioni at the University of Washington • The approach has attracted a good deal of attention, although

apparently so far only a prototype implementation exists

Overview of the method • The system analyses logs of the use of a given web site •

S proposes new index pages, each of which contains a number of related links that are likely to be of interest to the same type of user

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Facilitating Navigation / Adaptive Web Sites

64

Introduction and Overview (2)

63

4 Music Machines

2

Music Machines

1. Boss Dr-110 samples 2. Boss Dr-55 samples 3. Korg KPR-77 samples 4. Korg KR-55 samples 5. Linn LinnDrum samples


Music Machines


Music Machines Music Machines

Music

1. Boss Dr-110 samples 2. Boss Dr-55 samples 3. Korg KPR-77 samples 4. Korg KR-55 samples Machines 5. Linn LinnDrum samples



Music Machines

Music Machines



Music Machines


1

3

5

Introduction and Overview (3) • IndexFinder generates a candidate index page • The human web master accepts or rejects the candidate page • If the page is accepted, IndexFinder creates a final version of the

page and adds it to the web site • The web master specifies •

the page’s title

•

where in the site it should be linked

• Rejected pages are discarded

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Facilitating Navigation / Adaptive Web Sites

66

The Basic Data

Figure 1. Typical user access logs, these from a computer science Web site. Each entry corresponds to a single request to the server and includes originating machine, time, and URL requested. Note the series of accesses from each of two users (one from SFSU, one from UMN). 24hrlab-214.sfsu.edu - - [21/Nov/1996:00:01:05 -0800] "GET /home/jones/collectors.html HTTP/1.0" 200 13119 24hrlab-214.sfsu.edu - - [21/Now/1996:00:01:06 -0800] "GET /home/jones/madewithmac.gif HTTP/1.0" 200 855 24hrlab-214.sfsu.edu - - [21/Nov/1996:00:01:06 -0800] "GET /home/jones/gustop2.gif HTTP/1.0" 200 25460 x67-122.ejack.umn.edu - - [21/Nov/1996:00:01:08 -0800] "GET /home/rich/aircrafts.html HTTP/1.0" 404 617 x67-122.ejack.umn.edu - - [21/Nov/1996:00:01:08 -0800] "GET /general/info.gif HTTP/1.0" 200 331 203.147.0.10 - - [21/Nov/1996:00:01:09 -0800] "GET /home/smith/kitty.html HTTP/1.0" 200 5160 24hrlab-214.sfsu.edu - - [21/Nov/1996:00:01:10 -0800] "GET /home/jones/thumbnails/awing-bo.gif HTTP/1.0" 200 5117

• Typical user access logs, these from a computer science web site • Each entry corresponds to a single request to the server • It includes the originating machine, the time, and the URL requested • Note the series of accesses from each of two users (one from

SFSU, one from UMN).

Inner Workings of IndexFinder

66

IndexFinder consists of three basic modules: 1. The log processing module takes the web server access logs and computes how often pages co-occur in user visits 2. The cluster mining module takes this information and a graph of the web site and finds clusters of frequently co-occurring pages 3. The conceptual clustering module uses conceptual descriptions of the site s pages to convert these clusters into coherent concepts • These are output as candidate index pages and presented to the web master.

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Facilitating Navigation / Angara Converter

68

Angara Converter Experience of the Unknown Visitor (1)

The slides in this section are based on the "tour" offered at http://www.angara.com.

67

Experience of the Unknown Visitor (2)

68

• The Fashion Store highlights its most frequently purchased apparel •

• •

• • • •

item on the home page, a men’s leather jacket Although this week’s featured item effectively draws men into the site, women must click through to specific departments before finding merchandise of personal interest This means conversion rates are not optimized The Fashion Store is experiencing the same phenomenon as most of the on-line industry: • The majority of browsers visit a home page and leave before going deeper into the site, or purchase product, because the home page information is targeted to only one audience Increasing the conversion rates for browsers to buyers will require targeted, relevant content Over 90% of visitors to Web sites are unknown Industry conversion rates for new visitors average between 1 and 2% Site abandonment rates are 85% at the home page

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69


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Targeting Content: The Process (1)

Targeting Content: The Process (2)

70

• The Fashion Store came to Angara for help to develop a strategy to

increase their conversion rates while maintaining their uncluttered home page • We worked with them to understand key business drivers and,

drawing on extensive segmentation expertise, helped develop a segmentation scheme for The Fashion Store’s site that ultimately raised conversion rates two-fold

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Targeting Content: The Results

Now, unknown visitors to the Fashion Store site are presented a customized home page presenting targeted "Feature Items" based on their prospect category − defined by geographic and demographic variables such as age and gender.

Measurement and Reporting of Results (1)

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Measurement and Reporting of Results (2)

• Real-time access to reporting through a secure Website provides

The Fashion Store with detailed information to illustrate the improvement in conversion rates of unknown visitors based on targeting • These reports provide the information necessary for The Fashion

Store to quickly react to trends in customer behavior • Custom content can be frequently modified at The Fashion Store

site in order to surface new offers, react to inventory changes, or address other changes in market conditions

Calculating Return on Investment

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Facilitating Navigation / 7d b-bot

76

7d b-bot Introduction

Company URL: http://www.7d.net. The graphics in the following slides were supplied by Christoph Hölscher.

75

Background • 7d is a relatively new company, located in Hamburg, which is now

introducing its first products

The "b-bot" • We will look mainly at the system b-bot • The main function of this product − at least initially − is the

recommendation of informative web pages within large web sites

Overview of Recommendation Approaches (1) 76 • One of the distinguishing characteristics of 7d’s approach is shown

in the following two graphics • Three different recommendation techniques are integrated, which

are usually found in different systems: 1. Content-based recommendation 2. Behavior-based recommendation 3. Rule-based recommendation

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77


78

Overview of Recommendation Approaches (2)

Content-Based Recommendation

Goal: Given a particular document, recommend similar documents

78

79

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Behavior-Based Recommendation

• In the example,

U is assigned to the second (red) group on the

basis of his page selections to date • Accordingly, S recommends pages that members of this group have visited and/or rated positively • Information about users consists mainly of page selections (as opposed to explicit ratings)

URL of ATG: http://www.atg.com/

Rule-Based Recommendation

This graphic indicates how rules are defined and applied in the b-bot

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81

ATG’s Dynamo Personalization Server and two other personalization servers are analyzed in depth by: Fink, J., & Kobsa, A. (2000). A review and analysis of commercial user modeling servers for personalization on the world wide web. User Modeling and User-Adapted Interaction, 10, 209−249.

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Related Examples From ATG (1)

Even without technical knowledge, an administrator can define user groups in terms of particular attributes

Related Examples From ATG (2)

In a similar way, the administrator can specify which objects within a given category should be shown to which groups

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83

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Support for Analysis and Evaluation

As with many other personalization servers, a monitoring component allows various types of analysis of user’s behavior

Comparison Comparison of Approaches (1) Clixsmart Navigator

Adaptive Information Server

Adaptive Web Sites

Angara Converter

84

7d b-bot

1. Are adapations made to individual users or to groups? Individuals

Individuals

To date no tailoring; Groups development of tailoring for subgroups is planned

Individuals and/or groups

Generation of new index pages

Changes in content, layout, and/or structure

2. What form do adaptations take? Rearrangement of menu hierarchies

Rearrangement of options within menus

Changes in content of pages

3. To what extent do the adaptations demand U’s attention? Unexpected changes might be distracting

Unexpected changes might be distracting

No attention demanded

U may never notice adaptation

(Depends on particular form of adaptation)

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85

Clixsmart Navigator

Facilitating Navigation / Comparison

86

Comparison of Approaches (2) Adaptive Information Server

Adaptive Web Sites

Angara Converter

7d b-bot

4. What sort of explicit input is U required to supply? None

None

No explicit input (later Some specification of version: perhaps a bit) demographic information

In some cases: ratings, specification of demographic information

5. How much additional effort is required to administer the system? Little or no domain-specific administration

Little or no domain-specific administration

Collection and use of Webmaster postprocesses marketing data proposed index pages

(Depends on parts of system used)

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Allowing Configuration / MyYahoo!

88

Allowing Configuration Background

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• Yahoo! was one of the first web portals to employ personalization

methods • MyYahoo!, introduced in July, 1996, offers a rich variety of

personalization options, which have been used by millions • The following slides

http://www.yahoo.com

1. illustrate some of the ways in which MyYahoo! supports configuration by the user 2. summarize some lessons learned over the years by the designers of MyYahoo!

A User’s Main Page

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89

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90

Changing the Layout of a Page

Changing the Content of a Page

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92

Changing the Content of a Module

Adding a Page With One Click

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After reaching an interesting page that is not currently included in his main page, the user can have it included simply by clicking on the "Add to MyYahoo!" button at the upper right

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Creating New Pages

More ambitious users can create additional pages with collections of links

The remaining slides in this section contain abbreviated excerpts from the following article: Manber, U., Patel, A., & Robison, J. (2000). Experience with personalization on Yahoo! Communications of the ACM, 43(8), 35−39.

Configuration by the User

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Personalization within individual modules • For example, users can choose which TV channels they want to

include in their TV guide in addition to which local cable system they use

Simplification of configuration actions • Modules can be selected from a (long) list, but can also be added by

clicking on a button at the original content page • For example, every weather page (http://weather.yahoo.com)

contains an "Add to My Yahoo!" button, which adds that page directly to the user’s My Yahoo! page • Also, each module on a My Yahoo! page has an edit and a remove

button, allowing users to manipulate their pages directly, without ever needing to visit an edit/layout screen

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Adaptation by the System

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Simple automatic adaptation of content • Example: a sports module that lists the teams in the user’s area

after obtaining that information from U’s profile

Adaptation of search results • In some cases we can complement the usual Web search with

direct, focused content that can sometimes be personalized • For example, if

U searches for the name of a current movie, we

point to Yahoo! movies, show an image from the movie, the cast, and a pointer to a page with current showtimes • If

U looked at the showtimes page previously and entered a zip

code, that page is automatically customized to show the movie theaters near U • With one click after searching for a movie name,

showtimes in U’s area

U can see the

Predictability as a Paramount Goal

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• Most users expect to have at least an intuitive notion of what is

given to them, and they expect to see the same behavior consistently • Being surprised is wonderful if it is entirely a positive surprise, but

overall, being unpredictable is a negative • In particular, if people are not sure how things work, they are less

prone to experimentation, because they are afraid of breaking something, or getting into a state that cannot be undone • In the case of news, for example, it is not clear that people want

personal news; they often want the same news everyone else is getting • Getting local weather and news about a local sports team from zip

codes is obvious • Getting news about cancer because the user read some medical

journals in the past or searched for some medical terms can confuse the user at best, and at worst, can jeopardize user trust and raise serious privacy concerns in the user’s mind

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Other Lessons From MyYahoo!

• Most users take what is given to them and never customize • A great deal of effort should go into the default page • Power users will do amazing things; never underestimate them • People generally don’t understand the concept of customization

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Allowing Configuration / InfoBeans

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InfoBeans Background

This and the following slides include abbreviated excerpts from: Bauer, M., & Dengler, D. (1999). InfoBeans: Configuration of personalized information assistants. In M. T. Maybury (Ed.), IUI99: International Conference on Intelligent User Interfaces. New York: ACM.

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Origins • InfoBeans was developed as a research prototype at DFKI • It offers users novel ways of configuring web pages • Some of its most important properties are illustrated in the example

scenario shown on the following slides

Concepts • An InfoBean is a configurable component that either • •

encapsulates an existing information service; or specifies a process of information gathering and providing

• It communicates with its environment through channels which pass

information to or obtain data from other components • A collection of InfoBeans with a common user interface (a WWW

document accessible by a standard Web browser) is called an InfoBox

Example Scenario •

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U is a business traveler who plans all her trips using a web-based interface to an international flight and hotel reservation system

• She wants to have access to

1. detailed hotel information 2. a city map showing the location of the hotel 3. local weather data • She has found excellent web-based services for each of these

features • Since similar information is required several times, she wants to be

able to access only one page that provides her all relevant data quickly and automatically

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Example of an InfoBox

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Use of the InfoBox • The figure shows the resulting web page • When

U now submits new hotel request information, the two

InfoBeans considering weather and hotel information are immediately activated • The city map InfoBean starts working after the hotel information

InfoBean has passed the street name into its output channel

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Configuration Procedure (1)

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•

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U loads the InfoBox start page and has access to 1. predefined InfoBeans 2. her own earlier specified InfoBoxes 3. InfoBeans and the option to add or change things

•

U decides to add an InfoBox called "Business Travel" •

•

Her browser jumps to a new nearly empty page containing only the InfoBean toolbar

U defines a new InfoBean to encapsulate her usual travel reservation service

•

U is asked to draw a region on the still empty InfoBox page that should serve as the display area of this InfoBean

•

U specifies it as a pure encapsulation: •

She simply enters the URL of the reservation service

Configuration Procedure (2)

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• The top left frame is the HTML form for requesting hotel information • The relevant data to be caught for delivery with respect to a current

stay are 1. the name of the city 2. the hotel 3. the date • An InfoBean provides the ability to specify output channels easily via

direct manipulation • In the case of defining the output channel City this means that the

user selects the text input field named ’City’ • The InfoBean compiles this selection into the appropriate wrapper

action in order to gather the specified information for delivery from the document

Color profile: Generic CMYK printer profile Composite Default screen

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Figure 5.7 from: Bauer, M., & Paul, G. (2001). Programming by demonstration for information agents. In H. Lieberman (Ed.), Your wish is my command: Programming by example (pp. 87−114). San Francisco: Morgan Kaufmann.

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Specifying More Complex Wrappers (1) Figure 5.7

An infobox like the one shown in the video A sample InfoBox.

Specifying More Complex Wrappers (2)

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• If time permits, a video will be shown that illustrates how a userS can

specify a relatively difficult wrapper

V:\002564\002564.VP Monday, December 18, 2000 12:42:22 PM

R L

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Allowing Configuration / Comparison

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Comparison 107

Comparison of Approaches

MyYahoo!

InfoBeans

1. To what extent does U configure S? To a great extent, in part with especially convenient methods

To a great extent, with intelligent support

2. To what extent does S configure itself spontaneously? In carefully selected cases

S interprets U’s specifications in an intelligent way

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Using Lifelike Characters / The Notebook Expert

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Using Lifelike Characters Self-Description of Soliloquy (1)

See the Corporate Fact Sheet, which is available via http://www.soliloquy.com. A similarly upbeat description of the firm is given in the following article: Lucente, M. (2000). Conversational interfaces for e-commerce applications. Communications of the ACM, 43(9), 59−61.

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From the Corporate Fact Sheet "Soliloquy’s Dialogue Experts allow shoppers to interact with a website in the most natural and intuitive way: by using their own words" "And the Dialogue Experts’ knowledge about products is vast and available instantly, at any time" "Shoppers benefit from an enhanced user experience, better service and instant response time" "E-commerce websites benefit from higher sales, lower costs and invaluable market intelligence gained from the Dialogue Mining of shoppers’ conversations"

Self-Description of Soliloquy (2)

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Clients • The example dialogs with the Notebook Expert that are presented

on the following slides were conducted at http://www.buy.com in December, 2000 • At that time, the Notebook Expert was also deployed at

http://www.cnet.com • At both sites, the Notebook Expert has apparently since been taken

out of the site • Similarly, other earlier clients of Soliloquy (Acer, Hewlett-Packard,

and Hardware Street) have apparently discontinued use of the Notebook Expert

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Examples of Possible Inputs

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According to the web site, the Notebook Expert can deal with user inputs of the following types, among others:

Statements about the planned use of the system

Specification of preferences • I want a cheap notebook

• I travel a lot

• I plan to spend around

• I need a laptop for school

$2000 • Show me a notebook with a big screen • Show me a really fast laptop computer

• I need a laptop for • • • • •

accounting I do desktop publishing I trade in stocks, shares, options and futures I play games on the computer I need a laptop for graphics Show me one for spreadsheets

Questions about concepts • What is USB? • What is a sound card?

Dialog With the Notebook Expert (1)

When U arrives at buy.com’s main notebook page, she can sees the link (on the right) to the page of the Notebook Expert

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Here, S starts by asking about the features that U would like In some cases, S starts with a question about how U intends to use the notebook (see the examples above)


As we can see in the Quick Summary on the right, the vague specification "a lot of memory" (see the previous slide) was translated into the internal specification "> 64 Megabyte"

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The value "$3000" in the vague specification "I can spend about $3000" (previous slide) was interpreted not as an upper limit but as a desired price At any time, U can click on one of the hyperlinks in the window listing the notebooks to go to a web page about that specific notebook


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Here we can see how S interpreted the vague specification "I don’t need more than 600 mhz"

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Here is an example of an attempt by S to answer a question by U about a particular concept (here: a particular manufacturer)


Here is another example of S’s natural language understanding capabilities

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S is actually capable of processing some statements of this general sort E.g.,. "I go to conferences a lot" It would be unreasonable to expect S to be able to understand every possible statement of this type, since this capability would require a great deal of world knowledge Still, an interpretation problem should obviously not have such drastic consequences for the course of the dialog

Artificial Life Bot on Artificial Life Main Page

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Using Lifelike Characters / Artificial Life

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Advertised Functions of Bots

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From a product brief available from http://www.artificial-life.com.

What Can a Bot Do for You? • Create individual customer profiles, which includes information

customers are seeking • Provide detailed information to your customer that leads to sales • Facilitate customer awareness regarding products and services • Offer a patient and insightful presence which improves customer

loyalty • Enhance the customer’s experience through productive, natural

language conversations with your bot

Example Dialog (1)

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• The following slides show a dialog conducted with Luci • The bot’s output (small white letters) appears before the user types

the next input into the white field above it • The gestures and varying facial expressions of the bot are not captured in these screen shots, because they appear only briefly before she returns to her standard pose and expression

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Example Dialog (2)

Example Dialog (3)

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Example Dialog (4)

Example Dialog (5)

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Example Dialog (6)

Example Dialog (7)

Here, instead of typing a question, the user clicks on the link "Einstein ALife"

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Example Dialog (8)

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When the new page appears, Luci introduces its main character

The PPP Persona More information on PPP, including the publications cited below, is available from http://www.dfki.de/imedia/ppp/.

Overview of PPP

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History • In a series of research projects at DFKI since 1989, techniques for

automatic interactive presentations have been developed • In the project PPP ("Personalized Plan-Based Presenter"), one of

the first "personas" was introduced in 1994 • This line of research is now being pursued in the project Miau (see

below)

Relationship to other personas • The PPP persona employs an especially large repertoire of

behaviors • Its behaviors are planned for large presentation units • Since the focus is on presentation techniques, in most cases no

language input of the user is processed

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PPP: Example (1)

André, E., Rist, T., & Müller, J. (1998). Guiding the user through dynamically generated hypermedia presentations with a life-like character. In J. Marks (Ed.), IUI98: International Conference on Intelligent User Interfaces (pp. 21−28).New York: ACM.

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Using Lifelike Characters / The PPP Persona

PPP: Example (2) • • •

•

•

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U wants to spend holidays in Finland and is looking for a lakeside cottage S retrieves matching offers from the WWW, selects one of them, and presents it to U So that U can ask for more information, several items in the text are mouse−sensitive • Clicking on one of these items will lead to the insertion of a subscenario For instance, if the user clicks on the fishing item while the first cottage is presented, S will interrupt the current presentation and run a script with fishing possibilities • After that, S will continue with the main script and describe the next offer Note that following a navigation link does not cause paging, as it does with most conventional web presentations • Rather, a new presentation script for the agent along with the required textual and pictorial material is transferred to the client−sidepresentation runtime engine

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Self-Behaviors (1)

Self-Behaviors (2)

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• The persona’s primary purpose is to execute presentation acts • But its behavior is not only determined by the directives (i.e.,

presentation tasks) specified in the script • In addition, self−behaviors are indispensable in order to increase the

Persona’s vividness and believability • Though it is certainly possible to include appropriate instructions

directly in the presentation script, the approach here is to have them determined automatically

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Automatic Generation of Presentations

• Since the available cottages and also their features may change at

any time, it doesn’t make sense to rely on predesigned presentations • The generation process is therfore automated; it comprises the

following tasks: 1. the design of a multimedia discourse structure reflecting how the single parts of a presentation are related to each other 2. the decomposition of the presentation into self−contained presentation units 3. the design of a navigation graph 4. the design of presentation scripts for each presentation unit

See, among others, André, E., Rist, T., & Müller, J. (1999). Employing AI methods to control the behavior of animated interface agents. Applied Artificial Intelligence.

Experimental Evaluation

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Variants that were compared 1. Presentations with the persona, as was explained above 2. The same presentations without the visible persona, only with speech output and pointing with a cursor

Main results 3. The presence of a persona invoked positive affective reactions in the users 4. The objectively measurable uptake of information was neither better nor worse with the persona • One possible explanation is that, in this study, the behavior of the persona conveyed little information beyond mere pointing and speaking

Conclusion • An animated human-like figure can improve subjective acceptance −

without distracting users from the content of the presentation

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Using Lifelike Characters / The MIAU Agents

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The MIAU Agents Overview of MIAU

MIAU-Homepage: http://www.dfki.de/imedia/miau/. Publication: Elisabeth André, T. R., (2000). Presenting through performing: On the use of multiple lifelike characters in knowledge-based presentation systems. In H. Lieberman (Ed.), IUI 2000: International Conference on Intelligent User Interfaces (p. 1). New York: ACM. http://lieber.www.media.mit.edu/people/lieber/IUI/

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Basic ideas • The main concept in MIAU is that of a team of several agents that

converse about a product that U is interested in

Noninteractive MIAU • In the initial version, U does not communicate with the agents • Instead, U watches the agents converse • But U can set the parameters that determine the interaction Interactive MIAU • In a version currently under development (shown first in this tutorial,

with a video) U can direct utterances to the agents

Advantages claimed for the use of multiple agents • Different points of view and types of information can often be

presented especially effectively by different agents • Repetitions of claims (e.g., about the advantages of a product) are less boring to U when they are generated by different agents

Interactive MIAU: Examples (1)

Robby (left) is helping out by answering the more technical questions; he has just reported the (high) gas consumption of the car

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Because of his personality parameters, Robby expresses agreement with U’s negative assessment


Merlin, who has the personality parameters of a normal salesman, expresses his dismay and disbelief through gestures and facial expressions

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Setting the Presentation Parameters

In this screen, U determines which agents are to appear and what general sort of behavior they are supposed to exhibit

Noninteractive MIAU: Examples (1)

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The potential customer, here played by the agent Genie (right) has just expressed some concerns about the car in question; the salesman Merlin (right) attempts to downplay these concerns

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The next two slides show an example of a dialog that was conducted with the following agents and parameters:

Agent

Role

Personality Factors

Interests

Robby

Seller

Extraverted, agreeable

Sportiness

Peedy

Buyer

Introverted, disagreeable Environment

Merlin

Buyer

Extraverted, agreeable

Safety


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Agent

Utterance

Commentary

Robby:

Hello, I’m Robby. What can I do for you? We are interested in this car. This is a very sporty car. It can drive 100 miles per hour.

Initiates the conversation, because of extraversion Answers the question, because of extraversion Emphasizes a value dimension that is important to U; mentions an attribute that has positive implications with respect to this dimension Asks questions because of extraversion; wants to know about airbags because this attribute concerns the dimension of safety, which is important to him

Merlin: Robby:

Merlin:

Does it have airbags?

Robby: Merlin:

Sure. Excellent.

Positive evaluation, because the agent is "agreeable"; strong formulation because of extraversion

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Agent ... Peedy: Robby: Peedy: Robby:

Utterance

Commentary

How much gas does it consume? It consumes 8 liters per 100 km. Isn’t that bad for the environment? Bad for the environment? It has a catalytic converter. It is made of recyclable material.

Gas consumption has implications with respect to the environment

Negative comment because the agent is "disagreeable"

REA

Image from http://justine.www.media.mit.edu/people/justine/.

REA: An Embodied Information Presenter

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Using Lifelike Characters / REA

Example Dialog (1)

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Cassell, J. (2001). Embodied conversational agents: Representation and intelligence in user interface. AI Magazine, 22. http://justine.www.media.mit.edu/people/justine/publications.html

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• The following records an actual interaction between a user and Rea • Mike approaches the projection screen. Rea is currently turned side

on and is gazing idly about • As Mike moves within range of the two cameras mounted above the screen, Rea turns to face him REA: Hello. How can I help you? MIKE: I’m looking to buy a place near MIT. Rea nods, indicating that she is following. REA: I have a house to show you. A picture of a house appears on-screen behind Rea who blinks and looks at the house and then at Mike. REA: It’s in Somerville. MIKE: Tell me about it. Rea looks up and away while she plans what to say

Example Dialog (2)

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REA: It’s big. Rea makes an expansive gesture with her hands. Mike brings his hands up as if he is about to speak and so Rea does not continue; instead waiting for him to speak. MIKE: Tell me more about it REA: Sure thing. It has a nice garden. Rea sketches a curved gesture with her hands indicating that the garden extends along two sides of the house MIKE: How far is it? REA: It is five minutes to the Porter Square T station. Rea makes it clear that it is five minutes on foot from the T station by making a walking gesture with her fingers MIKE: How big is the house?

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Example Dialog (3)

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REA: It has four bedrooms, three bathrooms. . . Mike interrupts Rea who stops speaking immediately. MIKE: Wait. Tell me, where is the master bedroom? REA: I’m sorry, I didn’t catch that. What did you ask me? MIKE: Where is the master bedroom? REA: It’s upstairs. Rea points up MIKE: Where is the master bathroom? REA: It’s next to the bedroom. Rea brings her hands together to indicate the relationship between the bedroom and the bathroom. And the house tour continues ...

Cassell, J., Bickmore, T., Vilhjálmsson, H., & Yan, H. (2000). More than just a pretty face: Affordances of embodiment. In H. Lieberman (Ed.), IUI 2000: International Conference on Intelligent User Interfaces (pp. 52−59). New York: ACM. http://lieber.www.media.mit.edu/people/lieber/IUI/

Functions of Nonverbal Behaviors (1)

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Conversational functions and behavioral realizations (1) Communicative Functions

Communicative Behavior

Initiation and termination Reacting

Short glance

Inviting contact

Sustained glance, Smile

Distance salutation

Looking, Head toss/nod, Raise eyebrows, Wave, Smile

Close salutation

Looking, Head nod, Embrace or handshake, Smile

Break away

Glance around

Farewell

Looking, Head nod, Wave

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Functions of Nonverbal Behaviors (2)

Conversational functions and behavioral realizations (2) Communicative Functions

Communicative Behavior

Turn-Taking Give turn

Looking, Raise eyebrows (followed by silence)

Wanting turn

Raise hands into gesture space

Take Turn

Glance away, Start talking

Feedback Request feedback

Looking, Raise eyebrows

Give feedback

Looking, Head nod

Comparison Comparison of Approaches (1) Soliloquy

ALife-WebGuide

PPP

MIAU

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REA

1. To what extent does U have to install additional software on her own computer? No

No

No

Microsoft Agents; interactive version not yet web-capable

System exists only as prototype

2. What sort of language or speech generation is S capable of? Filling in of templates

Canned texts

Reading aloud of pre-stored texts

Reading aloud of texts generated with scripts

Generation of speech coordinated with nonverbal behaviors

3. What sorts of input can the persona understand, aside from typed text? None

None

(No free language input); selection from presented questions and evaluations

Specification of dialog parameters via user interface

Speech and nonverbal behaviors

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Soliloquy

Using Lifelike Characters / Comparison

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Comparison of Approaches (2) ALife-WebGuide

PPP

MIAU

REA

Facial expressions and gestures as supported by Microsoft agents

Facial expressions and gestures

4. What means of emotional expression does S have? Only verbal expressions

Sometimes appropriate facial expressions

Dynamically generated facial expressions and gestures

5. What other nonverbal means of expression does S have? Dynamically generated tables

Glances toward relevant parts of screen

Pointing coordinated with speaking

Occasional Gestures with meaningful considerable gestures (e.g., semantic content looking into a book)

6. To what extent is the behavior of S coordinated with the other aspects of U’s interaction with S?

U can pursue links to other pages

Often helpful comments that complement web pages

Tight linking between persona’s behavior and other elements of the presentation

(There are no other S’s behavior is aspects of the coordinated with interaction) display of pictures

Comparison of Approaches (3) Soliloquy

ALife-WebGuide

PPP

MIAU

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REA

7. To what extent does S help U to concentrate her attention on the pursuit of her goal? Excessive structuring of the dialog

OK, when understanding is successful

Strong focusing, despite U’s freedom of action

Complete focus on the product discussion, no other options for U

Complete focus on the product discussion, no other options for U

8. To what extent is U made to feel that she is being recognized and treated as an individual? Longer-term but simple adaptation

Only short-term adaptation to U

Some longer-term adaptation, but not always visible to U

Agents’ utterances are responsive to U’s

Fine-grained responses to U’s behavior