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Next Item Recommendation With Self-attention

Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun . Arxiv 2018 – 70 citations

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Affective Computing Compositional Generalization Datasets Interactive Environments Model Architecture Tools

In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user’s historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user’s transient interests. The model is finally trained in a metric learning framework, taking both short-term and long-term intentions into consideration. 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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