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arXiv · 1805.07037

MARS: Memory Attention-Aware Recommender System

Lei Zheng·Chun-Ta Lu·Lifang He·Sihong Xie·Vahid Noroozi·He Huang·Philip S. Yu
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Abstract

In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users' various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep \textit{adaptive user representations}. Trained in an end-to-end fashion, MARS adaptively summarizes users' interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.

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Lei Zheng, Chun-Ta Lu, Lifang He, Sihong Xie, Vahid Noroozi, He Huang, Philip S. Yu. 2018-05-18. MARS: Memory Attention-Aware Recommender System. https://arxiv.org/abs/1805.07037

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