A Modular Approach for User Modelling Ilaria Torre Dipartimento di Informatica - Università di Torino Corso Svizzera 185 - 10149 Torino (Italy)
[email protected]
Abstract. Adaptive hypermedia systems are spreading widely in these last years, but each application uses its own models and techniques. What I am studying is the possibility of developing a framework for user modelling in adaptive systems and in particular of creating a library of stereotypes for representing the different dimensions of the user models.
1
Introduction
Differently from other concepts and labels, adaptivity has the peculiarity that it does not pertain a specific type of application but it is rather a modus operandi that may concern any application that interacts with a human or software agent. In fact, if we regard the latter as a system having its own goals and thus some interests that it pursues, then an adaptive application may exploit these interests and goals to satisfy the needs of the software agent. Providing the “right” service (the “right” pieces of information) in the “right” way and at the “right” time is the main goal of an adaptive system. However, defining what “right” means in these contexts is a difficult task. This difficulty (ambiguity) is the main problem of adaptive systems in general and of adaptive hypermedia in particular. In the latter, in fact, the leverage to infer the model and the goals of a user is limited and also the range of alternative forms of personalization is restricted (as regards what has to be shown and/or hidden and how the user can be guided in the navigation). And even though a “correct” model is available and “correct” strategies for extracting information are used, there are still other fundamental problems such as: how to avoid that the choices of the system, tailored to the needs of the user, block her/his curiosity and stimuli for innovation and discovery? How to involve the user in the personalization process, that is: should all the choices be made without involving her/him or should the user be active in the process of selecting the service to be provided (or the pieces of information to be presented), or finally should adaptation be a co-operative process? How and to what extent should the user be allowed to access/inspect/modify her/his model? The approach to these problems depends only partially on the technologies that are adopted while it is strictly dependent on the psychological and cognitive choices that impact the various phases of the process: how data are gathered from a user, how the user model is built, how the feedbacks from the user are collected and used to update (revise) her/his model, which features of the user model are related to the type and
amount of information to be presented and to the form of the presentation. The various applications in the literature faced these problems using different strategies and formalisms, depending on aspects such as the application task (and domain), the goals of the system, etc. Goal. The goal of my research work is to study whether it is possible to abstract from specific application tasks and domains in order to build a reference framework for user modelling in adaptive systems. My study will first focus on the attempt to define: (i) a precise classification of application tasks and domain, (ii) a taxonomy of goals and (generic) sub-goals for adaptive systems and (iii) a set of conceptual dimensions for user modelling derived from the previous steps. Then I will concentrate on studying and relating these dimensions to create an ontology for user modelling, separating the dimensions that are general and may be related to different goals, tasks and domains from those that are more specific and task or domain dependent. The framework resulting from this work could have a significant impact on the construction of adaptive systems. On the one hand, it could simplify the analysis that has to be performed when designing a new application, providing guidelines to analyse the goals and the application task and domain and then suggesting the dimensions of user modelling that are most relevant. On the other hand, the modularity of the framework could allow the re-use of user modelling components across different applications. Methodology. In order to investigate the possibility of building the framework mentioned above, I will start from Brusilovsky’s classification of adaptive hypermedia [3] (and the subsequent analysis by De Bra [4]), focusing on the specific topic of the creation and management of the user model and I will perform a systematic analysis of adaptive systems with the aim of building the ontology discussed above. According to the terminology in [3], this will mean focusing on defining a correlation between: (i) “what features of the user are used as a source of adaptation” and (ii) “where adaptive hypermedia can be helpful” and the “adaptation goals”. Instead, the criteria concerning the strategies for adapting the presentation (“what can be adapted” in [3]) will not be the focus of this analysis. The bottom-up approach will start from an analysis of specific adaptive systems, in different tasks. This will involve both the design of adaptive applications and the analysis of systems described in the literature and implemented in the field. The dimensions that are relevant in user modelling will emerge from such a study and will be progressively refined and aggregated. Each dimension will partition the population of users into classes that will be described using stereotypes [5][6], as it is common in many systems. I will adopt a standard formalism in which each stereotype is formed by a set of slots (corresponding to features of the user) and each slot contains a set of pairs . Thus, besides the framework, one of the results of my work will be the creation of a library of stereotypes, grouped into families according to the conceptual dimensions. For each family, a set of metadata will provide information on the stereotypes in the family, about their generality and about the adaptation goals and application tasks/domains for which the family is relevant. It is well known that stereotypes are not sufficient for all aspects of user modelling; therefore, while in the first phase of
my work I am concentrating on this formalisms, in a second phase I will also consider other formalisms that are more suitable for capturing dynamic aspects of user modelling (as, for example, rules for updating/refining the user model after tracking the user’s behaviour).
2 Conceptual Dimensions in User Modelling As noticed above, the dimensions that are relevant in a user model are strongly related to the application task and to the specific goals of the adaptive system. Therefore, it is necessary to analyse more deeply these two aspects to determine the dimensions in a precise way. As regards the former, it is important to isolate specific application domains and features for each task; as regards the latter, it is fundamental to decompose the goal of a system into sub-goals which may be intermediate goals or goals related to a specific functionality of the overall system. The approach I am experimenting in my research work is that of building some matrices having on one of the axes the goals and sub-goals of the adaptive system and on the other the specific features of the application tasks and domains. Then each entry, corresponding to a specific row and column, describes in a precise way the characteristics of a specific adaptive application, and thus, for each entry, one can isolate the relevant dimensions as regards user modelling. The most interesting consideration that emerged from a first analysis is that while the high-level goals of a system are related to the application context, the sub-goals are often common to many applications and thus the dimensions of user modelling related to these sub-goals can be re-used for different tasks and applications. An example of a sub-goal (function) that is common to all the application domains that I analysed up to now (and that I will mention later) is that one of offering users the possibility of getting more information about an item. This sub-goal is strictly related to a dimension of user modelling: the level of receptivity of the user, which is thus common to many applications. An interesting direction for future research is thus the study of the sub-goals mentioned above, in order to analyse them precisely, trying to make them generic and then to isolate a structured library of goals and of the corresponding dimensions for user modelling. As regards the high-level goals and application domains, on the other hand, the analysis up to now does not evidence strict correlations with user modelling dimensions. However, the use of different application domains is important to weigh the contribution of different user modelling dimensions that, although common to a particular task may have different relevance in the different domains. Let me consider an example. In a recommender system the high level goal could be selling some product or service, providing recommendations (with the aim of selling something) or providing advice or comparative information. At a deeper level the goal of selling can be specialised distinguishing between the goals of leading the user to an “impulsive” purchase or to a “rational” one. In this latter case a sub-goal will be the presentation of detailed information and so forth. As regards the application domain, a first distinction is the one related to the type of product that is sold (e.g., a product of mass market vs. a sophisticated and complex product requiring configuration or
special demonstrations and assistance). A second distinction is the one concerning the market to which the product is directed (e.g., business to business or business to consumer). In a matrix like the one discussed above, for example we could have an entry corresponding to the (sub)goal of leading the user to an “impulse purchase” and to the domain feature “mass market product”. The most relevant user modelling dimension associated with such an entry is the one concerning the “life-style” of the users, while other dimensions such as the user expertise or experience are less relevant. Having a matrix providing this information is very useful, allowing the designer to re-use the user modelling knowledge bases in the library for different recommender systems and to decide the specific relevance of each one of the selected dimensions. In conclusion, I report in the following a first classification of the application tasks that I defined starting from the adaptive hypermedia in the literature (including those that we are designing and that we plan to deign): • recommender systems (e.g., e-commerce, advertisement systems, etc.); • access to information sources (e.g., news servers, information retrieval); • education systems; • decision support systems (e.g., trading on line); • applications for co-operative work.
3 Adaptive News Servers The first task that I considered is that of “adaptive news servers”; this work led to the design and implementation of a prototype personalized newspaper [1][2]. Referring to the previous methodology of classification, the system presents the following domain features: the service deals with large amounts of information (news), on different topics (organized in a hierarchy of sections), including very detailed pieces of information and various kinds of links between different pieces. The goal of the system is that of providing personalized views of the sections and subsections and of the specific news. This involves selecting and ordering sections and subsections and presenting news items at the detail level which is appropriate for each specific user. These goals distinguish, in my view, adaptive news server from adaptive information retrieval (which are separate sub-tasks in the matrices on which I am working); in the latter, in fact, the user has an active role (searching for some specific information) and the system provides a personalized answer to such a need (selecting the appropriate pieces of information). Three dimensions emerged as relevant for user modelling in this application: the interests of the user, her/his expertise, her/his receptivity. Dealing with interests is fundamental for the goal of selecting the sections/subsections that are most relevant for the user. Receptivity is critical for deciding the appropriate detail level and for tailoring the presentation to the capability of the user (i.e., for deciding the amount of information that (s)he can read/process). This dimension allows one to decide how many (sub)sections must be presented and how many details should be provided for news items. The second dimension (expertise) is in some sense intermediate between the other two, allowing one to vary the detail level in different sub(sections). For
example, if the user is very interested in a topic (corresponding to a (sub)section), has medium receptivity but limited expertise, then the presentation will not be very detailed but the (sub)section will be put in a prominent position among the other ones. As noticed in the paragraph concerning methodology, the knowledge regarding each dimension is represented using stereotypes which relate classificatory features with predictions concerning the dimension. Thus the news server application is based on the following three families of stereotypes: Interests: this family of stereotypes classifies the users according to their interests in the topics of the (sub)sections of the news server. Starting from classificatory data such as the age, gender, type and field of work, the scope of the connection (e.g., work or personal reasons), the stereotypes make a prediction on the interest level (which may be “high”, “medium”, “low” or “null”) in the topic of each (sub)section. Expertise: these stereotypes make use of classificatory data such as the education level (and type), the field of work and make predictions on the users' expertise in a set of domains (which are related to the (sub)sections of the news server). Receptivity: these stereotypes make predictions on the user’s level of receptivity using classificatory data such as the user’s age, education level, type of work, frequency of access to the WWW. An example of stereotype in this family (the “Medium receptive adult reader”) is reported in the following: Profile: age: 14-19: 0.00 | 20-25: 0.10 | 26-35: 0.20 | 36-45: 0.40 | 46-65: 0.20 | >65: 0.10 education level: primary school: 0.10 | secondary school :0.70 | university: 0.20 job: manager: 0.00 | self-trader: 0.10 | self-employed: 0.35 | employee: 0.35, etc. frequency of access to WWW: less than once a month: 0.00 | about once a week: 0.80, etc. Prediction:
receptivity level: high: 0.00 | medium: 1.00 | low: 0.00 In conclusions, the designed application seems to suggest (as shown also by the example) that there are dimensions of user modelling that are general and that can be re-used across different tasks and application domains. In this specific case, this holds for all the dimensions, especially as regards the classificatory part of the stereotypes. Indeed a preliminary study of other application tasks (recommender systems for ecommerce) provides a first confirmation of such a claim (and some of the dimensions above can be re-used also in this different application). However, a definite answer on which dimensions can be generalized and re-used across multiple tasks and which one are more specific will be possible only after analysing and decomposing all the tasks for various application domains.
References 1. Ardissono, L., Console, L., Torre, I.: Strategies for personalizing the access to news servers. In Proc. AAAI Spring Symposium on Adaptive User Interfaces, Stanford, (March 2000) 7– 12 2. Ardissono, L., Console, L., Torre, I.: On the application of personalization techniques to news servers on the WWW. In Lamma, E., Mello, P. (eds.): Advances in Artificial
Intelligence. Lecture Notes in Computer Science, Vol. 1792. Springer Verlag, Berlin Heidelberg New York (2000) 261–271 3. Brusilovsky, P.L.: Methods and Techniques of Adaptive Hypermedia. User Modelling and User-Adapted Interaction 6 (1996) 87–129 4. De Bra, P.: Design issue in adaptive hypermedia application development. In Second International Workshop on Adaptive Systems and User Modelling on the World Wide Web. Banff, Canada (1999) 29–39. Also available at http://www.contrib.andrew.cmu.edu/~plb/WWWUM99_workshop 5. McTear, M.F.: User modelling for adaptive computer systems: a survey of recent developments. Artificial Intelligence Review 7 (1993) 157–184 6. Rich, E.: Stereotypes and user modelling. In Wahlster, W., Kobsa, A. (eds): User Models in Dialog Systems, Springer Verlag, Berlin Heidelberg New York (1989) 35–51
