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.
|Title of host publication||Proceedings of the 18th Annual Conference on Artificial Intelligence and Cognitive Science|
|Editors||Sarah Jane Delany, Michael Madden|
|Place of Publication||Dublin|
|Number of pages||10|
|Publication status||Published - 2007|
|Event||18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2007) - Dublin, Dublin, Ireland|
Duration: 29 Aug 2007 → 31 Aug 2007
|Conference||18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2007)|
|Abbreviated title||AICS 2007|
|Period||29/08/07 → 31/08/07|
Rafter, R., Coyle, L., Nixon, P., & Smyth, B. (2007). Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation. In S. J. Delany, & M. Madden (Eds.), Proceedings of the 18th Annual Conference on Artificial Intelligence and Cognitive Science (pp. 170-179). Cahill Printers.