Abstract
Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user’s (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged in directing search at recommendation time. Such interactions can range from high-level dialogues with the user, possibly in natural language, to more simple interactions where the user is, for example, asked to indicate a preference for one of k suggested items. Importantly, the feedback attained from these interactions can help to differentiate between the user’s long-term stored preferences, and her current (short-term) requirements, which may be quite different. We argue that such interactions can also be beneficial to collaborative recommendation and provide experimental evidence to support this claim.
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References
Balabanovic, M. (1998). An Interface for Learning Multi-topic User Profiles from Implicit Feedback. In Proceedings of AAAI Workshop on Recommender Systems. Madison Wisconsin, USA
Billsus, D. & Pazzani, M. (1999). A Hybrid User Model for News Story Classification. In Proceedings of 7th International Conference On User Modelling (UM99), 99–108. Canada: Banff
Bridge, D. (2002). Towards Conversational Recommender Systems: A Dialogue Grammar Approach. In Proceedings of the Workshop in Mixed-Initiative Case-Based Reasoning, Workshop Programme at the Sixth European Conference in Case-Based Reasoning, 9–22. Scotland: Aberdeen
Goker, M. & Thompson, C. (2000). The Adaptive Place Advisor: A Conversational Recommendation System. In Proceedings of the 8th German Workshop on Case Based Reasoning. Germany: Lammerbuckel
Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., Riedl J. (1997). GroupLens: Applying Collaborative Filtering to Usenet News. Communications of ACM 40(3): 77–87
Koychev, I. & Schwab, I. (2000). Adaptation to Drifting User’s Interests. In Proceedings of ECML2000 Workshop: Machine Learning in New Information Age, 39–45. Spain: Barcelona
McGinty, L. & Smyth, B. (2002). Comparason-Based Recommendation. In Proceedings of the Sixth European Conference on Case-Based Reasoning (ECCBR-02), 575–589. Aberdeen, Scotland: Springer-Verlag
Rafter, R., Bradley, K. & Smyth, B. (2000). Automated Collaborative Filtering Applications for Online Recruitment Services. In Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, 363–368. Trento, Italy: Springer-Verlag
Rafter, R. & Smyth, B. (2001). Passive Profiling from Server Logs in an Online Recruitment Environment. In Proceedings of IJCAI Workshop on Intelligent Techniques for Web Personalization (ITWP2001), 35–41. Seattle, Washington, USA
Rafter, R. & Smyth, B. (2004). Towards Conversational Collaborative Recommendation. In Proceedings of the 15th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2004). Castlebar, Mayo, Ireland
Sarwar, B. M., Karypis, G., Konstan, J. A. & Riedl, J. (2001). Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International World Wide Web Conference (WWW10), 285–295. Hong Kong: ACM Press
Shimazu H. (2002). ExpertClerk: A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence Review. 18(3–4): 223–244
Widyantoro, D. H., Ioerger, T. R. & Yen, J. (1999). An Adaptive Algorithm for Learning Changes in User Interests. In Proceedings of the Eighth International Conference on Information and Knowledge Management (CIKM ’99), 405–412. Kansas City, Missouri, USA: ACM Press
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Rafter, R., Smyth, B. Conversational Collaborative Recommendation – An Experimental Analysis. Artif Intell Rev 24, 301–318 (2005). https://doi.org/10.1007/s10462-005-9004-8
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DOI: https://doi.org/10.1007/s10462-005-9004-8