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||Unknown Host Publication|
|Number of pages||0|
|Publication status||Published - 2007|
|Event||Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS07) - Dublin Institute of Technology, Ireland|
Duration: 1 Jan 2007 → …
|Conference||Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science (AICS07)|
|Period||1/01/07 → …|