稀疏数据集协同过滤算法的进一步研究

doi:10.11896/j.issn.1002-137X.2014.06.052

摘要/Abstract

摘要： 协同过滤算法是电子商务和信息系统中非常重要的一门技术。其中用户相似度度量方法的科学性至关重要。为了获得更好的精度,采用用户间共同评分数目来动态调节原相似度,以更准确地反映用户间相似度的真实性。在此基础上,根据社会网络中FTL模型(follow the leader)的思想,对新用户或找不到最近邻的用户采用基于专家信任度的预测算法代替传统相似度来预测用户的评分,弥补了传统算法的不足。实验表明,算法提高了预测评分的准确性和推荐质量,并缓解了新用户的冷启动问题。

Abstract: In electronic commerce and information system,collaborative filtering is a very important technique．User similarity measure of the scientific method is crucial.In order to obtain better accuracy,numbers of common Ratings between users were used to dynamically adjust the original similarity to more accuratelly reflect the authenticity of the similarity between users．On this basis,according to the social network FTL model (follow the leader) thoughts,for new users or users who cannot find the nearest neighbor,prediction algorithm based on expert trust degree was used instead of similarity to predict the user’s score,making up the deficiency of the traditional algorithm．Experiments show that the algorithm can improve the prediction score,the accuracy and the quality of recommendation,and alleviate the cold-start problem for new users.

Key words: Collaborative filtering,Recommendation algorithm,Similarity,Cold start

罗琦,缪昕杰,魏倩. 稀疏数据集协同过滤算法的进一步研究[J]. 计算机科学, 2014, 41(6): 264-268. https://doi.org/10.11896/j.issn.1002-137X.2014.06.052

LUO Qi,MIAO Xin-jie and WEI Qian. Further Research on Collaborative Filtering Algorithm for Sparse Data[J]. Computer Science, 2014, 41(6): 264-268. https://doi.org/10.11896/j.issn.1002-137X.2014.06.052

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