计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 181-187.doi: 10.11896/jsjkx.201100031

• 数据库&大数据&数据科学 • 上一篇    下一篇

多空间交互协同过滤推荐

李康林1,2, 古天龙2, 宾辰忠2   

  1. 1 桂林电子科技大学电子工程与自动化学院 广西 桂林541004
    2 桂林电子科技大学广西可信软件重点实验室 广西 桂林541004
  • 收稿日期:2020-11-03 修回日期:2021-03-01 出版日期:2021-12-15 发布日期:2021-11-26
  • 通讯作者: 宾辰忠(binchenzhong@guet.edu.cn)
  • 作者简介:1808305012@mails.guet.edu.cn
  • 基金资助:
    国家自然科学基金项目(62066010,61862016,61966009);广西自然科学基金项目(2020GXNSFAA159055);广西创新驱动重大专项项目(AA17202024)

Multi-space Interactive Collaborative Filtering Recommendation

LI Kang-lin1,2, GU Tian-long2, BIN Chen-zhong2   

  1. 1 School of Electronic Engineering & Automation,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2020-11-03 Revised:2021-03-01 Online:2021-12-15 Published:2021-11-26
  • About author:LI Kang-lin,born in 1996,postgra-duate.His main research interests include recommendation system and data mining.
    BIN Chen-zhong,born in 1979,Ph.D.His main research interests include da-tamining and intelligent recommendation.
  • Supported by:
    National Natural Science Foundation of China(62066010,61862016,61966009),Natural Science Foundation of Guangxi Province(2020GXNSFAA159055) and Innovation-Driven Major Projects of Guangxi Province(AA17202024).

摘要: 大数据时代,由于信息过载,用户很难从海量数据中寻找出感兴趣的内容,个性化推荐系统的诞生极好地解决了这个问题。协同过滤算法被广泛应用于个性化推荐领域,但由于模型的限制,推荐效果未能得到进一步提升。现有的基于协同过滤模型的改进方法大多都是通过引入表示学习方法来得到更好的用户表示向量和项目表示向量,或通过改进用户项目匹配函数来提升推荐能力,但此类工作都致力于从单个交互提取用户-项目交互信息。文中提出了一种多空间交互协同过滤推荐算法,将用户向量和项目向量映射到多空间,从多角度做用户-项目交互,使用两层注意力机制聚合最终的用户表示向量和项目表示向量,以进行评分预测。在公开的真实数据集上,多空间交互协同过滤模型(MSICF)与多个基线模型进行了对比实验,MSICF模型的评估优于对比的基线方法。

关键词: 推荐系统, 协同过滤, 多空间交互, 注意力机制

Abstract: In the era of big data,due to information overload,it is difficult for users to find interesting content from massive data.The birth of personalized recommendation system has greatly solved this problem.Collaborative filtering has been widely used in the field of personalized recommendation,but due to the limitations of the model,the recommendation effect has not been further improved.Most existing collaborative filtering introduce presentation learning methods to obtain better user representation vectors and item representation vectors or improve user item match functions to improve the performance of the recommendation system,but such work is devoted to extracting user-item interaction information from a single interaction.This paper proposes a multi-space interactive collaborative filtering recommendation algorithm,which maps user vectors and item vectors to multiple spaces,performs user-item interaction from multiple angles,and then uses a two-layer attention mechanism to aggregate the final user representation vector and item representation vector to make a score prediction.The multi-space interaction collaborative filtering(MSICF) was compared with baseline on the published real dataset,and the evaluation of the MSICF is better than baseline.

Key words: Recommender systems, Collaborative filtering, Multi-space interaction, Attention mechanism

中图分类号: 

  • TP331
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