MARS: Memory Attention-Aware Recommender System
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.
Cite
@article{arxiv.1805.07037,
title = {MARS: Memory Attention-Aware Recommender System},
author = {Lei Zheng and Chun-Ta Lu and Lifang He and Sihong Xie and Vahid Noroozi and He Huang and Philip S. Yu},
journal= {arXiv preprint arXiv:1805.07037},
year = {2018}
}