Collaborative filtering via graph signal processing
Weiyu Huang, Antonio Garcia Marques, Alejandro Ribeiro · 2017
This paper develops new designs for recommender systems inspired by recent advances in graph signal processing. Recommender systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph filters, we demonstrate that a common collaborative filtering method - k-nearest neighbors - can be modeled as a specific band-stop graph filter on networks describing similarities between users or items. These new interpretations pave the way to new methods for enhanced rating prediction. For collaborative filtering, we develop more general band stop graph filters. The performance of our algorithms is assessed in the MovieLens-100k dataset, showing that our designs reduce the root mean squared error (up to a 6.20% improvement) compared to one incurred by the benchmark collaborative filtering approach.