MARS: Memory Attention-Aware Recommender System
| Autor: | Lifang He, Chaozhuo Li, Huang He, Lei Zheng, Philip S. Yu, Chun-Ta Lu, Vahid Noroozi, Sihong Xie, Bowen Dong |
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| Rok vydání: | 2019 |
| Předmět: |
Information retrieval
Recall Computer science business.industry Deep learning 02 engineering and technology Mars Exploration Program Recommender system ComputingMethodologies_PATTERNRECOGNITION 020204 information systems Component (UML) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business Interpretability |
| Zdroj: | DSAA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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