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