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  • 标题:An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation
  • 本地全文:下载
  • 作者:Yupeng Liu ; Yanan Zhang ; Xiaochen Zhang
  • 期刊名称:Discrete Dynamics in Nature and Society
  • 印刷版ISSN:1026-0226
  • 电子版ISSN:1607-887X
  • 出版年度:2021
  • 卷号:2021
  • 页码:1-12
  • DOI:10.1155/2021/6693730
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Users’ reviews of items contain a lot of semantic information about their preferences for items. This paper models users’ long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model. Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews. Given a sequence of neighbor users of a user, we design a hierarchical attention model to capture union-level preferences on sequential patterns, a pointer model to capture individual-level preferences, and a traditional attention model to balance the effects of both union-level and individual-level preferences. Finally, the long-term and short-term preferences are combined into a representation of the user and item profiles. Extensive experiments demonstrate that the model substantially outperforms many other state-of-the-art baselines substantially.
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