Next Item Recommendation with Self-Attention

TL;DR

This paper introduces a self-attention based recommendation model that effectively captures user interests from interaction sequences, outperforming existing methods across various datasets.

Contribution

It presents a novel sequence-aware recommendation approach utilizing self-attention and metric learning to improve user interest modeling.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effectively captures both short-term and long-term user interests

Demonstrates significant improvement in recommendation accuracy

Abstract

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.