Recommender Systems Using Harmonic Analysis

Recommender systems provide recommendations on variety of personal activities or relevant items of interest. They can play a significant role for E-commerce and in daily personal decisions. However, existing recommender systems still face challenges in dealing with sparse data and still achieving high accuracy and reasonable performance. The issue with missing rating leads to inaccuracies when trying to match items or users for rating prediction. In this paper, we propose to address these challenges with the use of Harmonic Analysis. The paper extends on our previous work, and provides a comprehensive coverage of the method with additional experiments. The method provides a novel multiresolution 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. The evaluation of the system shows superiority of the solution compared to state of the art solutions for user-based collaborative filtering and item-based collaborative filtering.