GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Artificial Intelligence Review
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Evaluating the impact of different virtual characters in product recommendations
IHC '06 Proceedings of VII Brazilian symposium on Human factors in computing systems
Using Matrix Model to Find Association Rule Core for Diverse Compound Critiques
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Rapid development of knowledge-based conversational recommender applications with advisor suite
Journal of Web Engineering
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.