The Footprint of Factorization Models and Their Applications in Collaborative Filtering

Abstract

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering (CF). However, the intermediate data generated in factorization models’ decision making process (or training process, footprint) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization (MF) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent (SGD), alternating least squares (ALS), and Markov Chain Monte Carlo (MCMC)). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top-N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.