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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and retrieval – information filtering. H.3.3 [Information Storage and Retrieval]: Systems and Software – User profiles and alert services General Terms Algorithms, Design, Experimentation. Keywords Multi-Criteria Recommender System, Relevant Set Correlation, Clustering

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Nkechi Nnadi
• Keywords: Control Systems, Multi-criteria Rating, Recommender System, RECSYS 2009, Relevant Set Correlation