Social recommendation algorithms with user feedback information
Social recommendation algorithms with user feedback information
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带有用户反馈信息的社交推荐算法
DOI:
10.1002/cpe.5934
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发表时间:
2020-07
期刊:
影响因子:
--
通讯作者:
Dongsheng Wang
中科院分区:
文献类型:
--
作者:
Yuecheng Yu;Yu Gu;Huayu Zuo;Jinlei Wang;Dongsheng Wang
Social media information can effectively improve the performance of personalized recommendation model. However, the feedback information in the social media which can accurately reflect users' implicit preferences is often ignored by most existing methods. To improve the users' experience and reduce the push of unwelcome information, in this article, we propose a new social recommendation algorithm with user feedback information. Different from the existing recommendation methods based on probability matrix decomposition, we incorporate the user implicit feedback information into the user rating prediction function. To reduce the data sparsity of implicit feedback information, we also adopt social network trust calculation in our algorithm. As a result, we can not only optimize the recommendation list but also filter out most of disgusting content. Compared with PMF, UserCF, CUNE, and TrustSVD, but slightly lower than RSTE, the experimental results of our model on real‐world datasets demonstrate the effectiveness of our proposed method, and further verify that the user experience is significantly improved without obviously reducing the accuracy of the recommendation.
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