336 In Eds. Bullinger H. -J. & Ziegler J. "Human-Computer Interaction: Ergonomics and User Interfaces" Lawrence Erlbaum Associates Inc. Publishers London, Mahwah, New Jersey, 1999, 336-340.
Combining User and Usage Modelling for User-Adaptivity Systems Alexander Nikov*, Wolfgang Pohl** *Technical University of Sofia, PO Box 41, BG-1612 Sofia, Bulgaria email:
[email protected]. **GMD-FIT, HCI Research Group Schloss Birlinghoven, D-53754 St. Augustin, Germany, email:
[email protected]
1 Introduction User-adapted interaction has been a research goal for many years now. In research on natural-language dialog systems, user models were introduced as explicit representation of assumptions about the individual characteristics of users. Systems would consult these models to decide how to adapt their behaviour to each user. Traditional user models contain mentalistic assumptions (MA), i.e., assumptions about knowledge, goals, interests and other mental attitudes. They are typically represented explicitly in some symbolic or numeric format (Pohl 1998). The main problem in this traditional approach is how to acquire relevant assumptions about the user. In most systems, heuristics were used to control user model acquisition, which often led to unreliable system adaptivity. In recent years user-adaptive systems like interface agents and personal assistants (Maes 1994) have been developed that use a different approach: They analyse observations of user behaviour applying machine learning techniques, and adapt to the user based on usage patterns detected. Thus, these systems form behaviour-oriented assumptions (BOA) about the user in a systematical and reliable way, but without representing them explicitly (see Davidson and Hirsh 1998 for a recent example). Hence, the user cannot inspect and control the assumptions the system holds. Moreover, mentalistic assumptions needed by many adaptive applications are not constructed. Our idea is to show that both mentalistic user modelling and behaviour-based usage modelling can be beneficially used to develop user-adaptive systems. We propose the MBAUM (mentalistic and behaviour-oriented user modelling) framework that combines both approaches and is based on ideas described in
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(Pohl 1997) and (Nikov et. al 1997). Behaviour analysis is employed for systematic acquisition of BOA. MA are acquired from these BOA, hence also being based on reliable behaviour analysis. The framework has been applied to a server for information on research grants with the goals of both optimising the user interface in an individual way and implementing personalised suggestions of new research grants to users.
2 A Framework for Usage and User Modelling The aim of MBAUM (cf. Fig. 1) is to adapt interactive systems to the user based on a user model containing both BOA and MA. For this purpose, logfile data of user-system interaction, interviews with users and domain knowledge (which is user-independent) are used. An adaptation of interaction structures of the system based on a neuro-fuzzy algorithm – Fuzzy backpropagation (FBP) algorithm (Stoeva and Nikov 1999) is carried out. FBP algorithm combines neural networks and fuzzy logic. The results of FBP algorithm are also used to construct an explicit user model with BOA and MA that supports further adaptation. In Fig. 1, the data collected from user and interactive system consist of logfile, user interviews, and domain knowledge. Logfile data provides records of observed user (inter-) actions. Domain knowledge mainly contains rules that describe conditions for user satisfaction of work with the system. The initial interaction structures and their application functional interaction points (AFIP) are also represented within domain knowledge. For carrying out the neuro-fuzzy adaptation, the structure of training patterns and neural networks for processing these patterns are defined. The actual patterns are taken from logfile data using the transitions between each AFIP pair. From these patterns, the Fuzzy backpropagation algorithm (Stoeva and Nikov 1999) learns AFIP weights, developed by one of the authors, which are used to create optimal hierarchical interaction structures. These structures have minimal sum of weighted hierarchy path lengths. Learning results (AFIP weights and interaction structures) implicitly contain information about user behaviour, used to form the explicit BOA (interaction preferences and generic action sequences) within the user model. MA will be mainly constructed from BOA, but can also be derived from observations, both based on heuristic rules. The following adaptations are possible: Using domain knowledge, hierarchical interaction structures are enhanced to networked interaction structures; BOA are used for action prediction; and, based on MA, help and information are provided individually.
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data from user and interactive system
adapted interactive system
neuro-fuzzy adaptation training patterns
neural networks
AFIPs
inputs
structure determination
AFIP weights
logfile observed user actions dialog history
targets
weights training
AFIP transition weights
interaction structures creation of optimal hierarchical interaction structures
user interviews
networked interaction structure
forecasting user action sequences
questionnaires structured interviews user model domain knowledge interaction structure constraints rules concepts
mentalistic assumptions user goals user beliefs user interests
adaptive help behavior-oriented assumptions interaction preferences generic action sequences
information provision
Figure 1. MBAUM framework.
3 Application The Electronic Funding Information server ELFI [http://www.elfi.ruhr-unibochum.de/elfi/] provides web-based access to information on research funding (cf. Fig. 2). Detailed descriptions of funding programs are maintained in a central database. The user retrieves needed information from this database using selection trees. When the user selects a tree item, appropriate funding information is listed, from which the user can choose. All user interactions with ELFI are recorded into a logfile, which provides the basic information for adaptivity. The selection trees represent interaction structures that can be adapted to the user. Fig. 3 shows a subtree of the selection tree ‘Funding organisation’ for selections based on the regional scope of funding, before and after adaptation. The number of selectable items (15) in the menu European countries was reduced to 3 menus with maximum 7 items each. The number of items (7) was chosen based on Miller’s number 7±2 (Miller 1956). Stating preferred country selection as explicit BOA further leads to the MA “interest in funding from country X”. Similar assumptions can be formed from the usage of selection trees concerning research topics and kind of grant (project, fellowship, travel grant, etc.). Thus, an explicit model of user information interest is formed. ELFI makes use of this model for information recommendation: Whenever a new research grant description is entered into the database, the model is consulted to determine whether the grant is of particular relevance to the user. If so, the user is informed about the new grant via email.
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Figure 2. ELFI interaction flow.
Figure 3. Initial (left) and adapted (right) ELFI selection subtrees of the selection tree ‘Funding organisation’.
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4 Conclusions The MBAUM framework and its application to the ELFI system show that a combination of usage modelling (realised by applying a novel neuro-fuzzy algorithm) and user modelling, of mentalistic assumptions and behaviouroriented assumptions is possible. Moreover, this combination is beneficial in the sense that it allows for rich user-adaptivity, related to both individualisation of the user interface and personalization of system functionality. This means an important improvement over previous approaches focusing on only one aspect of adaptivity. Further developments include refinement of MBAUM components and implementations in other application systems. Special attention will be paid to forecasting of user action sequences and to adaptive help.
5 References Davidson, B.D. & Hirsh, H. (1998). Probabilistic online action prediction. AAAI Spring Symposium on Intelligent Environments. Maes, P. (1994). Agents that reduce work and information overload. Communications of the ACM, 37(7), 31-40. Miller, G. A. (1956), The magical number seven plus or minus two, Psychological Review, 63, 81-87. Nikov, A., Delichev, S. & Stoeva, S. (1997). An neuro-fuzzy approach for user interface adaptation implemented in the information system of Bulgarian parliament, In Sepälä, P. Luopajärvi, T. Nygård, C. & Mattila, M. (Eds.): Human computer-interaction, Stress and mental load, Aging, Occupational health (Vol. 1) Proc. 13th International Ergonomics Congress, (IEA’97, Tampere, Finland, June 29 - July 4, 1997), pp. 100-112. Helsinki: Finnish Institute of Occupational Health. Pohl, W. (1997). LaboUr – Machine learning for user modelling. In Smith, M.J. et al. (Eds.): Proc. of HCI International ’97, Vol. B, pp. 27-31. Amsterdam: Elsevier Science. Pohl, W. (1998). Logic-Based Representation and Reasoning for User Modelling Shell Systems. St. Augustin, Germany: infix. Stoeva, S. & Nikov, A. (1999). A fuzzy backpropagation algorithm. Fuzzy Sets and Systems, accepted for publication.
