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.
This work was supported by a grant from the National Key Research and Development Program of China(2018YFC0809804), State Key Laboratory of Communication Content Cognition(Grant No.A32003), the Artificial Intelligence for Sustainable Development Goals(AI4SDGs) Research Program, National Natural Science Foundation of China(U1736103, 61976154, 61402323, 61876128), the National Key Research and Development Program(2017YFE0111900).
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Liu, J. et al. (2021). LoCo-VAE: Modeling Short-Term Preference as Joint Effect of Long-Term Preference and Context-Aware Impact in Recommendation. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_37
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