Dynamic Intention-Aware Recommendation with Self-Attention
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Predicting the missing values given the observed interaction matrix is a predominant task in the recommendation research field, which is well-suited to the case when long-term user preference is considered. However, ignoring timestamp information makes it impossible to detect interest drifts of individual users over time, while in many practical applications, both long and short-term intents are critical to the success of recommendation algorithms. In this paper, we aim to tackle the sequential recommendation problem by modeling these two types of intents in an integrated manner. Our model is structured into two components, one for short-term intents learning and the other one for long-term preference modeling. We propose capturing user's short-term interest with a self-attention mechanism which attends over the past behaviors in a supervised learning approach. The model is finally learned in a metric learning framework, which could overcome the weakness of dot product. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin.
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