计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (3): 637-648.DOI: 10.3778/j.issn.1673-9418.2009011

• 人工智能 • 上一篇    下一篇

耦合用户公共特征的单类协同过滤推荐算法

张全贵+(), 胡嘉燕, 王丽   

  1. 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105
  • 收稿日期:2020-09-07 修回日期:2020-11-06 出版日期:2022-03-01 发布日期:2020-11-19
  • 通讯作者: + E-mail: zhqgui@126.com
  • 作者简介:张全贵(1978—),男,河北秦皇岛人,博士,副教授,CCF会员,主要研究方向为深度学习、推荐系统。
    胡嘉燕(1993—),女,广东江门人,硕士研究生,主要研究方向为深度学习、推荐系统。
    王丽(1994—),女,辽宁阜新人,硕士研究生,主要研究方向为深度学习、推荐系统。
  • 基金资助:
    辽宁省自然科学基金指导计划项目(20180550995);辽宁省教育厅科学技术项目(LJ2019JL009)

One Class Collaborative Filtering Recommendation Algorithm Coupled with User Common Characteristics

ZHANG Quangui+(), HU Jiayan, WANG Li   

  1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Received:2020-09-07 Revised:2020-11-06 Online:2022-03-01 Published:2020-11-19
  • About author:ZHANG Quangui, born in 1978, Ph.D., associate professor, member of CCF. His research interests include deep learning and recommendation system.
    HU Jiayan, born in 1993, M.S. candidate. Her research interests include deep learning and recommendation system.
    WANG Li, born in 1994, M.S. candidate. Her research interests include deep learning and recommendation system.
  • Supported by:
    Natural Science Foundation of Liaoning Province(20180550995);Science and Technology Project of Liaoning Provincial Department of Education(LJ2019JL009)

摘要:

将显式特征与隐式反馈相结合是提高单类协同过滤(OCCF)推荐准确性的常用方法。但目前的研究一般是直接将原始显式特征或交叉特征集成到OCCF模型中,因其难以判断哪些显式特征是真正重要的,故很难获得显著的性能改进。基于此,提出了一种耦合用户公共特征的单类协同过滤推荐算法(UCC-OCCF)。首先,建立基于邻居的共同偏好表示网络(NB-CPR),学习与当前用户具有相似显式特征的邻居用户和某一类项目之间的交互关系,间接利用显式特征以获得共同偏好;然后,建立个人深度潜在因素表示网络(DLFR),使用深度神经网络学习用户-项目之间的潜在因素,从而得到当前用户与项目之间的交互概率;最后,基于邻居的共同偏好表示网络与个人深度潜在因素表示网络进行联合训练,从而将用户公共特征耦合到单类协同过滤推荐模型中,以提高推荐准确度。在公共数据集MovieLens 100K、MovieLens 1M和MyAnimelist上的实验结果表明,UCC-OCCF可以显著提高OCCF的推荐准确性。

关键词: 单类协同过滤(OCCF), 深度学习, 共同偏好, 隐式反馈, 显式特征

Abstract:

Combining explicit features with implicit feedback is a common method to improve the recommendation accuracy of one class collaborative filtering (OCCF). However, current studies generally integrate the original explicit features or cross features directly into OCCF models, which makes it difficult to determine which explicit features are really vital, so it is untoward to achieve significant performance improvement. To sum up, a one class collaborative filtering recommendation algorithm coupled with user common characteristics (UCC-OCCF) is proposed. First, the neighbor-based common preference representation network (NB-CPR) is established to learn the interaction between users with similar explicit characteristics as the current users and a certain type of item, and to indirectly use explicit characteristics to obtain common preferences. Then, the deep latent factors representation (DLFR) uses a deep neural network to learn the potential factors between the user and the item, thus obtaining the interaction probability between the current user and the item. At last, the NB-CPR is combined with the personal depth latent factor representation network for training, so as to couple the common characteristics of users into OCCF model to improve the recommendation accuracy. Experimental results on public datasets MovieLens 100K, MovieLens 1M and MyAnimelist, show that UCC-OCCF can significantly improve the recommendation accuracy of OCCF.

Key words: one-class collaborative filtering (OCCF), deep learning, common preferences, implicit feedback, explicit feature

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