applying relevant set correlation clustering to multi-criteria recommender systems
This thesis investigates application of clustering to multi-criteria ratings as a method of improving the precision of top-N recommendations. With the advent of ecommerce sites that allow multi-criteria rating of items, there is an opportunity for recommender systems to use the additional information to gain a better understanding of user preference. This thesis proposes the use of the relevant set correlation model for a clustering-based collaborative filtering system. It is anticipated this novel system will handle large numbers of users and items without sacrificing the relevance of recommended items.
relevant set correlation clustering multi-criteria recommender system