Bayesian Personalized Feature Interaction Selection for Factorization Machines

Abstract

Factorization Machines (FMs) are widely used for feature-based collaborative filtering tasks, as they are very effective at modeling feature interactions. Existing FM-based methods usually take all feature interactions into account, which is unreasonable because not all feature interactions are helpful: incorporating useless feature interactions will introduce noise and degrade the recommendation performance. Recently, methods that perform Feature Interaction Selection (FIS) have attracted attention because of their effectiveness at filtering out useless feature interactions. However, they assume that all users share the same feature interactions, which is not necessarily true, especially for collaborative filtering tasks. In this work, we address this issue and study Personalized Feature Interaction Selection (P-FIS) by proposing a Bayesian Personalized Feature Interaction Selection (BP-FIS) mechanism under the Bayesian Variable Selection (BVS) theory. Specifically, we first introduce interaction selection variables with hereditary spike and slab priors for P-FIS. Then, we form a Bayesian generative model and derive the Evidence Lower Bound (ELBO), which can be optimized by an efficient Stochastic Gradient Variational Bayes (SGVB) method to learn the parameters. Finally, because BP-FIS can be seamlessly integrated with different variants of FMs, we implement two FM variants under the proposed BP-FIS. We carry out experiments on three benchmark datasets. The empirical results demonstrate the effectiveness of BP-FIS for selecting personalized interactions and improving the recommendation performance.