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SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

机译：SemRec：一种基于加权异构信息网络的个性化语义推荐方法

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摘要

Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we introduce the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on three real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths. Moreover, extensive experiments validate the benefits of weighted meta paths.
机译：最近，异构信息网络（HIN）分析引起了很多关注，并且在HIN上开发了许多数据挖掘任务。作为重要的数据挖掘任务，推荐器系统包括许多对象类型（例如，电影推荐中的用户，电影，演员和兴趣组）以及对象类型之间的丰富关系，它们自然构成了HIN。 HIN的全面信息集成和丰富的语义信息使其有望产生更好的建议。但是，传统的HIN不考虑链接上的属性值，并且由于用户和项目之间存在评分（通常为1到5），HIN中广泛使用的元路径可能无法准确捕获对象之间的语义关系。在推荐系统中。在本文中，我们介绍了加权HIN和加权元路径概念，通过区分不同的链接属性值来巧妙地描述路径语义。此外，我们提出了一种基于语义路径的个性化推荐方法SemRec，以预测用户对商品的评分。通过设置元路径，SemRec不仅可以灵活地集成异构信息，还可以获得代表用户在路径上偏好的优先级和个性化权重。在三个真实数据集上进行的实验表明，SemRec通过在加权元路径的帮助下灵活地集成信息，可以实现更好的推荐性能。此外，大量实验验证了加权元路径的好处。

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  • 来源
    《World Wide Web》 |2019年第1期|153-184|共32页
  • 作者

    Shi Chuan; Zhang Zhiqiang; Ji Yugang; Wang Weipeng; Yu Philip S.; Shi Zhiping;

  • 作者单位

    Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China|Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing Shi, Peoples R China;

    Ant Financial Serv Grp, Hangzhou, Zhejiang, Peoples R China;

    Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing Shi, Peoples R China;

    Univ Southern Calif, Los Angeles, CA USA;

    Univ Illinois, Chicago, IL USA;

    Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Heterogeneous information network; Recommendation; Similarity; Meta path;

    机译：异构信息网络推荐相似性元路径;

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