Tariq Mahmood
Francesco Ricci
ABSTRACT
Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005. Google Scholar
Digital Library
- E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting web search result preferences. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 3--10, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- D. Aha and L. Breslow. Refining conversational case libraries. In Proc. ICCBR-97, pages 267--278. Springer, 1997. Google ScholarDigital Library
- L. Ardissono, G. Petrone, and M. Segnan. A conversational approach to the interaction with web services. Computational Intelligence, 20(4):693--709, 2004.Google ScholarCross Ref
- D. Billsus and M. Pazzani. Learning probabilistic user models. In UM97 Workshop on Machine Learning for User Modeling, 1997.Google Scholar
- R. Burke. Hybrid web recommender systems. In The Adaptive Web, pages 377--408. Springer Berlin / Heidelberg, 2007. Google ScholarDigital Library
- N. Golovin and E. Rahm. Reinforcement learning architecture for web recommendations. In Proc. ITCC'04, pages 398--402, 2004. Google ScholarDigital Library
- A. Goy, L. Ardissono, and G. Petrone. Personalization in e-commerce applications. In The Adaptive Web, pages 485--520. Springer Berlin / Heidelberg, 2007. Google ScholarDigital Library
- J. Henderson, O. Lemon, and K. Georgila. Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets. Computational Linguistics, 34(4):487--511, 2008. Google ScholarDigital Library
- T. Mahmood and F. Ricci. Learning and adaptivity in interactive recommender systems. In Proc. ICEC'07, pages 75--84, 2007. Google ScholarDigital Library
- T. Mahmood and F. Ricci. Adapting the interaction state model in conversational recommender systems. In Proc. ICEC '08, pages 1--10. ACM, 2008. Google ScholarDigital Library
- T. Mahmood, F. Ricci, A. Venturini, and W. Höpken. Adaptive recommender systems for travel planning. In W. H. Peter OConnor and U. Gretzel, editors, Proc. ENTER 2008, pages 1--11. Springer, 2008.Google Scholar
- L. McGinty and B. Smyth. Adaptive selection: An analysis of critiquing and preference-based feedback in conversational recommender systems. International Journal of Electronic Commerce, 11(2):35--57, 2006. Google ScholarDigital Library
- N. Mirzadeh and F. Ricci. Cooperative query rewriting for decision making support and recommender systems. Applied Artificial Intelligence, 21:1--38, 2007. Google ScholarDigital Library
- J. Nielsen. Usability Engineering. Academic Press, 1994. Google ScholarDigital Library
- J. Reilly, K. McCarthy, L. McGinty, and B. Smyth. Incremental critiquing. Knowledge-Based Systems, 18(4--5):143--151, 2005. Google ScholarDigital Library
- P. Resnick and H. R. Varian. Recommender systems. Communications of the ACM, 40(3):56--58, 1997. Google ScholarDigital Library
- G. Shani, D. Heckerman, and R. I. Brafman. An mdp-based recommender system. Journal of Machine Learning Research, 6:1265--1295, 2005. Google ScholarDigital Library
- S. P. Singh, D. J. Litman, M. J. Kearns, and M. A. Walker. Optimizing dialogue management with reinforcement learning: Experiments with the NJFun system. J. Artif. Intell. Res. (JAIR), 16:105--133, 2002. Google ScholarDigital Library
- R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998. Google ScholarDigital Library
- N. Taghipour and A. Kardan. A hybrid web recommender system based on q-learning. In Proc. 2008 ACM Symposium on Applied Computing, pages 1164--1168, 2008. Google ScholarDigital Library
- N. Taghipour, A. Kardan, and S. S. Ghidary. Usage-based web recommendations: a reinforcement learning approach. In Proc. 2007 ACM Conference on Recommender Systems, pages 113--120, 2007. Google ScholarDigital Library
- S. ten Hagen, M. van Someren, and V. Hollink. Exploration/exploitation in adaptive recommender systems. In Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, 2003.Google Scholar
- C. A. Thompson, M. H. Goker, and P. Langley. A personalized system for conversational recommendations. Artificial Intelligence Research, 21:393--428, 2004. Google ScholarDigital Library
- Y. Z. Wei, L. Moreau, and N. R. Jennings. Learning users' interests in a market-based recommender system. In Proc. IDEAL, pages 833--840, 2004.Google ScholarCross Ref
Index Terms
- Improving recommender systems with adaptive conversational strategies
Recommendations
Learning and adaptivity in interactive recommender systems
ICEC '07: Proceedings of the ninth international conference on Electronic commerceRecommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction session. Quite recently, conversational approaches have been introduced in ...
Adapting the interaction state model in conversational recommender systems
ICEC '08: Proceedings of the 10th international conference on Electronic commerceConventional conversational recommender systems support interaction strategies that are hard-coded into the system in advance. In this context, Reinforcement Learning techniques have been proposed to learn an optimal, user-adapted interaction strategy, ...
Unifying Recommender Systems and Conversational User Interfaces
CUI '22: Proceedings of the 4th Conference on Conversational User InterfacesThis paper considers unifying research on conversational user interfaces and recommender systems. Studies on conversational user interfaces (CUIs) typically examine how conversations can be facilitated (i.e., optimizing the means). Recommender systems ...
Comments