Next Item Recommendation with Self-Attention

Understand

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.