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:
Concurrency and Computation: Practice and Experience
影响因子:
--
通讯作者:
Dongsheng Wang
Dongsheng Wang
中科院分区:
其他
文献类型:
--
作者:
Yuecheng Yu;Yu Gu;Huayu Zuo;Jinlei Wang;Dongsheng Wang

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社会化媒体信息可以有效提高个性化推荐模型的性能。然而,社会媒体中的反馈信息,可以准确地反映用户的隐式偏好往往被大多数现有的方法忽略。为了提高用户体验,减少不受欢迎的信息推送,本文提出了一种新的社会推荐算法与用户反馈信息。与现有的基于概率矩阵分解的推荐方法不同,我们将用户的隐式反馈信息融入到用户评分预测函数中。为了降低隐式反馈信息的数据稀疏性,我们还在算法中引入了社会网络信任度计算。因此,我们不仅可以优化推荐列表,还可以过滤掉大部分令人厌恶的内容。在真实的世界数据集上的实验结果表明,与PMF、UserCF、CUNE和TrustSVD相比,该模型的推荐精度略低于RSTE,用户体验得到了显著改善,而推荐精度没有明显降低。
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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