Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation

R Rafter, Lorcan Coyle, Patrick Nixon, Barry Smyth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user's current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performing recommendation that incorporates information about the individual performance of neighbours during a recommendation session, into the neighbour retrieval mechanism. We present our architecture and a set of preliminary evaluation results that suggest there is some merit to our approach. We examine these results and discuss what they mean for future research.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages0
Publication statusPublished - 2007
EventProceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS07) - Dublin Institute of Technology, Ireland
Duration: 1 Jan 2007 → …

Conference

ConferenceProceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS07)
Period1/01/07 → …

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title = "Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation",
abstract = "Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user's current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performing recommendation that incorporates information about the individual performance of neighbours during a recommendation session, into the neighbour retrieval mechanism. We present our architecture and a set of preliminary evaluation results that suggest there is some merit to our approach. We examine these results and discuss what they mean for future research.",
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author = "R Rafter and Lorcan Coyle and Patrick Nixon and Barry Smyth",
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Rafter, R, Coyle, L, Nixon, P & Smyth, B 2007, Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation. in Unknown Host Publication. Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS07), 1/01/07.

Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation. / Rafter, R; Coyle, Lorcan; Nixon, Patrick; Smyth, Barry.

Unknown Host Publication. 2007.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation

AU - Rafter, R

AU - Coyle, Lorcan

AU - Nixon, Patrick

AU - Smyth, Barry

PY - 2007

Y1 - 2007

N2 - Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user's current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performing recommendation that incorporates information about the individual performance of neighbours during a recommendation session, into the neighbour retrieval mechanism. We present our architecture and a set of preliminary evaluation results that suggest there is some merit to our approach. We examine these results and discuss what they mean for future research.

AB - Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user's current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performing recommendation that incorporates information about the individual performance of neighbours during a recommendation session, into the neighbour retrieval mechanism. We present our architecture and a set of preliminary evaluation results that suggest there is some merit to our approach. We examine these results and discuss what they mean for future research.

KW - n/a

M3 - Conference contribution

BT - Unknown Host Publication

ER -