Modularized User Modeling in Conversational Recommender Systems Pontus warnestal Department of Computer Science, Linkoping University, Sweden o@ida.liu. se 1 Introduction ly research interest lies in investigating user-adaptive interaction in a conver sational setting for recommender systems, with particular focus on modularized user model components and the use of a dialogue partner(DP) in such systems Research on recommender systems have focused mostly on the back-end al- gorithms they employ, whereas front-end interaction issues have not been as horoughly studied [2, and most recommender systems rely on the graphical point-and-click metaphor. The area of conversational recommender systems ex- plores another interaction metaphor; that of natural language(NL) interaction in a dialogue (e. g. with spoken interaction [8, 5). Other recommender system approaches acknowledge the benefits of NL and / or dialogue-based interaction in their systems, but employ graphical user interfaces since their primary focus lies elsewhere(e. g. [1, 6 ). Underlying motivations for the conversational approach are that the initializing and updating of the user preference model can be carried out efficiently and enjoyably using natural language, that an on-going dialogue supports evolving user queries and continuous updating of user preference data, and that NL facilitates rich feedback in a way that is natural to end-users. A related metaphor--however often lacking proper conversational interac tion--is the virtual assistant approach, which takes the form of an animated character(e.g. the Microsoft Office assistant, and the SitePal guides)that pro- vides help with a software application or web site. Such virtual partners display a number of different interaction strategies and varying adaptive functionality User-adaptive functionality based on user models has long been proposed in order to meet individual needs and make interaction more usable and efficient 4 In order to make full use of and to better understand conversational dp interac- tion with user-adaptive recommenders, research addressing the following issues seems to be needed: (a)how to design and implement conversational virtual assis- tants,(b)how to endow such assistants with appropriate Nl dialogue capabilities to effectively render them as DPs, and(c)how to adapt DPs to individual users 2 Research problems Implementation details may vary, but essentially a generic NL dialogue system ar- chitecture can be described as consisting of the following phases(in order):(a)in- terpretation, (b)dialogue management, (c) domain reasoning, and(d)generation rdissono, P. Brna, and A. Mitrovic(Eds ) UM 2005, LNAI 3538, pp. 527-529, 2005Modularized User Modeling in Conversational Recommender Systems Pontus W¨arnest˚al Department of Computer Science, Link¨oping University, Sweden ponjo@ida.liu.se 1 Introduction My research interest lies in investigating user-adaptive interaction in a conver￾sational setting for recommender systems, with particular focus on modularized user model components and the use of a dialogue partner (dp) in such systems. Research on recommender systems have focused mostly on the back-end al￾gorithms they employ, whereas front-end interaction issues have not been as thoroughly studied [2], and most recommender systems rely on the graphical point-and-click metaphor. The area of conversational recommender systems ex￾plores another interaction metaphor; that of natural language (nl) interaction in a dialogue (e.g. with spoken interaction [8, 5]). Other recommender system approaches acknowledge the benefits of nl and/or dialogue-based interaction in their systems, but employ graphical user interfaces since their primary focus lies elsewhere (e.g. [1, 6]). Underlying motivations for the conversational approach are that the initializing and updating of the user preference model can be carried out efficiently and enjoyably using natural language, that an on-going dialogue supports evolving user queries and continuous updating of user preference data, and that nl facilitates rich feedback in a way that is natural to end-users. A related metaphor—however often lacking proper conversational interac tion— is the virtual assistant approach, which takes the form of an animated character (e.g. the Microsoft Office assistant, and the SitePal guides) that pro￾vides help with a software application or web site. Such virtual partners display a number of different interaction strategies and varying adaptive functionality. User-adaptive functionality based on user models has long been proposed in order to meet individual needs and make interaction more usable and efficient [4]. In order to make full use of and to better understand conversational dp interac￾tion with user-adaptive recommenders, research addressing the following issues seems to be needed: (a) how to design and implement conversational virtual assis￾tants, (b) how to endow such assistants with appropriate nl dialogue capabilities to effectively render them as dps, and (c) how to adapt dps to individual users. 2 Research Problems Implementation details may vary, but essentially a generic nl dialogue system ar￾chitecture can be described as consisting of the following phases (in order): (a) in￾terpretation, (b) dialogue management, (c) domain reasoning, and (d) generation. L. Ardissono, P. Brna, and A. Mitrovic (Eds.): UM 2005, LNAI 3538, pp. 527–529, 2005.
c Springer-Verlag Berlin Heidelberg 2005