Next Item Recommendation with Self-Attention
This addresses the problem of personalized recommendation for users by enhancing accuracy in predicting next items, though it appears incremental as it builds on existing self-attention and metric learning techniques.
The paper tackles the problem of next item recommendation by proposing a sequence-aware model that uses self-attention to infer item-item relationships from user interactions, achieving state-of-the-art performance with significant improvements across multiple datasets.
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.