Conversational collaborative recommendation - an experimental analysis

Files in This Item:
File Description SizeFormat 
paper45.pdf581.7 kBAdobe PDFDownload
Title: Conversational collaborative recommendation - an experimental analysis
Authors: Rafter, Rachael
Smyth, Barry
Permanent link:
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 filteringRecommender systemsConversational 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

Citations 20

Last Week
Last month
checked on Aug 9, 2018

Google ScholarTM



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.