Does weighting improve matrix factorization for recommender systems?

Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus

Track: User modeling, personalization and recommendation

Keywords: Recommender System; Collaborative Filtering; Autoencoder; Neighborhood Approach; Weighted Linear Regression; Matrix Factorization

Abstract: Matrix factorization is a widely used approach for top-N recommendations and collaborative filtering. When it is implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve the performance of certain algorithms. In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. Somewhat surprisingly, we find that the best performing methods, as measured by the standard (unweighted) ranking accuracy on publicly available datasets, are trained using unweighted data. This observation challenges the conventional wisdom in the literature. Nevertheless, we identify cases where weighting can be beneficial, particularly for models with lower capacity and certain regularization schemes. We also derive efficient algorithms for minimizing a number of weighted objectives which were previously unexplored due to the lack of efficient optimization techniques. Our work provides a comprehensive analysis of the interplay between weighting, regularization, and model capacity in matrix factorization for recommender systems.

Submission Number: 1971