结合物品相似性的社交信任推荐算法

doi:10.11896/jsjkx.210300217

计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 144-151.doi: 10.11896/jsjkx.210300217

• 数据库&大数据&数据科学 • 上一篇下一篇

结合物品相似性的社交信任推荐算法

余皑欣, 冯秀芳, 孙静宇

太原理工大学软件学院太原030024

收稿日期:2021-03-22 修回日期:2021-09-13 出版日期:2022-05-15 发布日期:2022-05-06
通讯作者: 冯秀芳(feng_xf2008@126.com )
作者简介:(yax_1203@163.com)
基金资助:
山西省重点研发计划(201903D121121)

Social Trust Recommendation Algorithm Combining Item Similarity

YU Ai-xin, FENG Xiu-fang, SUN Jing-yu

College of Software,Taiyuan University of Technology,Taiyuan 030024,China

Received:2021-03-22 Revised:2021-09-13 Online:2022-05-15 Published:2022-05-06
About author:YU Ai-xin,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include recommendation system and data mining.
FENG Xiu-fang,born in 1966,Ph.D,professor,is a member of China Computer Federation.Her main research interests include artificial intelligence,Internet of things and cloud computing.
Supported by:
Key Research and Development Plan of Shanxi Province(201903D121121).

摘要/Abstract

摘要： 随着互联网的快速发展,用户很难在大量的网络数据中找到自己感兴趣的内容,而推荐系统能帮助解决这一问题。传统的推荐系统仅依赖用户历史行为数据进行推荐,存在数据稀疏和冷启动的问题。将社交网络信息融入推荐系统中被证明能够有效地解决传统推荐系统的问题,提高了推荐质量。但是,大部分基于社交网络的推荐仅关注用户之间的单向信任关系,忽略了被信任关系和物品自身因素对推荐结果的影响,因此提出了结合物品相似性的社交信任推荐算法SocialIS。SocialIS算法考虑了用户作为信任者和被信任者时邻居用户对用户的影响,并采用Node2vec算法训练得到包含用户偏好的物品相似性向量,再使用图神经网络学习用户和物品的特征向量进行评分预测。在Epinions和Ciao数据集上进行了大量实验,采用基于误差的指标(MAE和RMSE)对所提算法的性能进行度量,并与其他算法进行对比,验证了所提算法的性能。实验结果表明,与其他算法相比,所提算法的评分预测误差更小,推荐效果更好。

关键词: Node2vec, 社交网络, 图神经网络, 推荐系统, 信任推荐

Abstract: With the rapid development of Internet,it is difficult for users to find the content they are interested in from massive network data,while the recommendation system can solve this problem.Traditional recommendation systems only rely on user’s historical behavior data for recommendation,which has the problems of data sparsity and cold start.The integration of social network information into the recommendation system has been proven to effectively solve the problems of the traditional recommendation system and improve the quality of recommendation system.However,most recommendation systems based on social networks only focus on the one-way trust relationships between users,and ignore the influence of the trusted relationship and the item’s own factors on recommendation results.Therefore,a social trust recommendation algorithm,called SocialIS,which combines item similarity,is proposed.The influence of neighbor users on user when the user is truster and trustee is considered by SocialIS,and the Node2vec algorithm is used to train the item similarity vector containing the user’s preference,and then the graph neural network is used to learn the feature vector of the user and the item to predict the score.A large number of experiments are conducted on the Epinions and Ciao data sets,and the performance of the proposed algorithm is measured by error-based indicators (MAE and RMSE),and compared with other algorithms to verify its performance.Experimental results show that compared with other algorithms,the proposed algorithm had smaller scoring prediction error and better recommendation effect.

Key words: Graph neural network, Node2vec, Recommendation system, Social network, Trust recommendation

中图分类号:

TP391

余皑欣, 冯秀芳, 孙静宇. 结合物品相似性的社交信任推荐算法[J]. 计算机科学, 2022, 49(5): 144-151. https://doi.org/10.11896/jsjkx.210300217

YU Ai-xin, FENG Xiu-fang, SUN Jing-yu. Social Trust Recommendation Algorithm Combining Item Similarity[J]. Computer Science, 2022, 49(5): 144-151. https://doi.org/10.11896/jsjkx.210300217

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结合物品相似性的社交信任推荐算法

Social Trust Recommendation Algorithm Combining Item Similarity

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