Artificial Intelligence Review

, Volume 24, Issue 3, pp 301–318

Conversational Collaborative Recommendation – An Experimental Analysis


DOI: 10.1007/s10462-005-9004-8

Cite this article as:
Rafter, R. & Smyth, B. Artif Intell Rev (2005) 24: 301. doi:10.1007/s10462-005-9004-8


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.


collaborative filteringconversational recommendationrecommender systems

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Adaptive Information Cluster, Smart Media Institute, School of Computer Science and InformaticsUniversity College DublinDublinIreland