协同过滤技术的改进研究  

An improved collaborative filtering algorithm

作　　者：刘国丽[1] 由志远[1] 李艳萍[1] 于丽梅[1] 

机构地区：[1]河北工业大学计算机科学与软件学院,天津300401

出　　处：《计算机工程与科学》2016年第5期997-1001,共5页Computer Engineering & Science

基　　金：河北省高等学校科学技术研究项目(ZD20131070)

摘　　要：协同过滤算法应用于个性化推荐系统中取得了巨大成功,它是通过用户项目评分数据,以用户之间或者项目之间相互协作的方式来产生推荐。然而,邻居用户的相似度计算不精确一直是阻碍推荐系统推荐精度进一步提高的主要因素。从提高用户间相似度计算精度出发,提出了一种改进算法,该算法通过考虑不同特征、加强平均值影响、惩罚热门项目的比重,对用户的相似度计算方法进行改进,以期生成更加合理的邻居用户集,最后,根据评分预测公式进行预测,最终产生推荐。在MovieLens数据集上的实验表明,改进算法计算用户间的相似度更加精确,推荐算法的预测精确度有了显著提高。The collaborative filtering algorithm applied to the personalized recommendation system is a great success, which generate recommendations through the users＇ rating data on the program and mu- tual cooperation between users or projects. However, the inaccurate similarity calculation among neigh- bors becomes the main obstacle to further improve the accuracy of the recommendation system. To im- prove the calculation accuracy of similarity among users, we propose an improved collaborative filtering algorithm by considering different characteristics, strengthening the mean effect and punishing the pro- portion of popular items, attempting to generate a more reasonable set of neighbor users. Finally we predict the scores according to the prediction equation and ultimate recommendations are generated. Ex- periments on the MovieLens datasets show that the proposed algorithm can calculate the similarity a- mong users more accurately, and the prediction accuracy is improved significantly.

关 键 词：协同过滤推荐 推荐精度 相似度 邻居用户集 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]