Abstract：Focusing on the issues of rating prediction such as subjectivity of user rating and inaccurate prediction caused by rating sparsity,a rating prediction method is proposed by introducing the characteristics of social recommendation.Firstly,aiming at the subjective of user rating,we introduce and optimize the cloud model theory.Then,a method to generate rating standard by synthetical cloud model and transform user rating under the standard is proposed.Secondly,to deal the problem of inaccurate score prediction caused by data sparseness,data dimension reduction and target user location are achieved by introducing membership degree.And taking into account that user rating can be affected by their social relationship,we try to learn two rating prediction models by respectively using social relationships and similar groups.Finally,the rating value is obtained by using Gauss transform to combine the two prediction models.Experimental results show that our method not only overcomes subjectivity of user rating,but also alleviates the poor accuracy caused by rating sparsity problem in traditional rating prediction methods.
肖云鹏, 孙华超, 戴天骥, 李茜, 李暾. 一种基于云模型的社交网络推荐系统评分预测方法[J]. 电子学报, 2018, 46(7): 1762-1767.
XIAO Yun-peng, SUN Hua-chao, DAI Tian-ji, LI Qian, Li Tun. A Rating Prediction Method Based on Cloud Model in Social Recommendation System. Acta Electronica Sinica, 2018, 46(7): 1762-1767.
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