Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation

• URL: http://arxiv.org/abs/2006.15346v2
• Date: Fri, 24 Jul 2020 06:43:00 GMT
• Title: Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation
• Authors: Jing Zhu, Yanan Xu and Yanmin Zhu
• Abstract summary: Session-based recommenders typically explore the users' evolving interests. Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. We propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation.
• Score: 17.092823992007794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of session-based recommendation is to predict the users' next clicked item, which is a challenging task due to the inherent uncertainty in user behaviors and anonymous implicit feedback information. A powerful session-based recommender can typically explore the users' evolving interests (i.e., a combination of his/her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are two main drawbacks. First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively. Second, current studies typically think long-term and short-term interests as equally important, but the importance of them should be user-specific. Therefore, we propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn user's short-term interest by taking into account the contextual information and temporal signals simultaneously. Besides, we introduce a gated fusion method that adaptively integrates the user's long-term and short-term preferences to generate the hybrid interest representation. Experiments on the three real-world datasets show that PAN achieves obvious improvements than the state-of-the-art methods.

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