A Multiresolution Approach to Recommender Systems

Abstract

Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach to the user-item matrix and extracts the interplay between users and items at multiple resolution levels. New affinity matrices are defined to measure similarities among users, among items, and across items and users. Furthermore, the similarities are assessed at multiple levels of granularity allowing individual and group level similarities. These affinity matrices thus produce multiresolution groupings of items and users, and in turn lead to higher accuracy in matching similar context for ratings, and more accurate prediction of new ratings. Evaluation results show superiority of the approach compared to state of the art solutions.