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MARS: Memory Attention-Aware Recommender System | IEEE Conference Publication | IEEE Xplore

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 repre...Show More

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 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.
Date of Conference: 05-08 October 2019
Date Added to IEEE Xplore: 23 January 2020
ISBN Information:
Conference Location: Washington, DC, USA

References

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