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.

中文翻译：

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具有用户反馈信息的社交推荐算法

社交媒体信息可以有效提高个性化推荐模型的性能。然而，社交媒体中能够准确反映用户隐性偏好的反馈信息往往被大多数现有方法所忽视。为了改善用户体验，减少不受欢迎信息的推送，本文提出了一种新的基于用户反馈信息的社交推荐算法。与现有基于概率矩阵分解的推荐方法不同，我们将用户隐式反馈信息纳入用户评分预测函数中。为了减少隐式反馈信息的数据稀疏性，我们在算法中也采用了社交网络信任计算。其结果，我们不仅可以优化推荐列表，还可以过滤掉大部分恶心的内容。与 PMF、UserCF、CUNE 和 TrustSVD 相比，但略低于 RSTE，我们的模型在真实世界数据集上的实验结果证明了我们提出的方法的有效性，并进一步验证了用户体验的显着改善，而没有明显降低建议的准确性。