Towards Conversational Collaborative Recommendation
|Title:||Towards Conversational Collaborative Recommendation||Authors:||Rafter, Rachael
|Permanent link:||http://hdl.handle.net/10197/4640||Date:||Sep-2004||Abstract:||Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations are 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 deep dialogues with the user that may involve natural language dialogues, 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 di erentiate between the user's long-term stored preferences, and her current (short-term) requirements, which may be quite di erent. We argue that such interactions can also be bene cial to collaborative recommendation and provide preliminary experimental evidence in support of this.||Type of material:||Conference Publication||Keywords:||Collaborative recommender systems||Language:||en||Status of Item:||Not peer reviewed||Conference Details:||The 15th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 04), Castlebar, Mayo, Ireland, 8-10 September, 2004|
|Appears in Collections:||Computer Science Research Collection|
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