Conversational collaborative recommendation - an experimental analysis
|Title:||Conversational collaborative recommendation - an experimental analysis||Authors:||Rafter, Rachael
|Permanent link:||http://hdl.handle.net/10197/4639||Date:||Nov-2005||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.||Type of material:||Journal Article||Publisher:||Springer||Copyright (published version):||2005, Springer Netherlands||Keywords:||Collaborative filtering; Recommender systems; Conversational recommendation||DOI:||10.1007/s10462-005-9004-8||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.