LoCo-VAE: Modeling Short-Term Preference as Joint Effect of Long-Term Preference and Context-Aware Impact in Recommendation

Abstract

User preference modeling is an essential task for online recommender systems. Recently, methods have been applied to model short-term user preferences within a short-term period. These approaches use recent user behavior as the context to determine the current short-term preferences. However, we argue that short-term user preferences are related to more complex contexts, e.g., the seasons or the time of the day. Furthermore, we make the hypothesis that short-term preferences of a user is actually a joint effect of his/her stable long-term preferences and the context-aware impact. Therefore, we propose LoCo-VAE, a unified model of this joint effect with Variational Auto-Encoder (VAE) based strategies. First, we utilize a Multilayer Perceptron(MLP) to capture long-term user preferences. Second, we improve the traditional VAE by distributing user interactions with respect to different contexts to introduce the context-aware impact. Finally, the long-term preferences and context-aware impact are combined with a joint generative training process to generate the embedding of short-term user preferences. Experiments on real-world datasets of Amazon consumption and music selection demonstrate the superiority of our model compare with state-of-the-art methods in recommendation system.