计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 68-71.

• CCML 2013 • 上一篇    下一篇

基于混合模型推荐算法的优化

李鹏飞,吴为民   

  1. 北京交通大学计算机与信息技术学院计算机工程系 北京100044;北京交通大学计算机与信息技术学院计算机工程系 北京100044
  • 出版日期:2018-11-14 发布日期:2018-11-14

Optimized Implementation of Hybrid Recommendation Algorithm

LI Peng-fei and WU Wei-min   

  • Online:2018-11-14 Published:2018-11-14

摘要: 现代电子商务系统用户和物品数目的日益增加使得User-Item矩阵变得越来越稀疏,再加上目前相似性度量方法均存在一定弊端,致使推荐系统的推荐质量降低了。针对传统混合模型推荐算法做了优化,其相似性度量方法由物品属性相似性和改进的修正余弦相似性线性组合而成,权重因子自动生成,考虑了用户评分尺度及用户活跃度对物品相似性的影响。为解决冷启动问题,使用用户基本信息获得用户间的相似度,各属性权重因子由SVDFeature计算得到。实验结果表明,该算法有效地提升了推荐系统的推荐质量,同时还有效解决了用户冷启动与物品冷启动问题。

关键词: 协同过滤,相似度,混合模型,权重因子,冷启动 中图法分类号TP181文献标识码A

Abstract: The ever-increasing number of users and it ems of modern electronic commercial system has made the user-item matrix to become more and more sparse.This situation,in combination with somewhat inappropriate similarity calculation methods currently used,maks the recommendation quality of recommender system to gradually reduce.For this,we presented an optimized recommender algorithm which is based on a hybrid model.In our algorithm,the similarity function is a linear combination of the item property similarity and a modified correlation cosine similarity.The weighting factor,which is generated automatically,is related to the number of users who rated both items.The modification to the correlation cosine similarity measure considers both the rating tendency and the activity from users.To deal with the cold start problem,we also acquired user similarity through user property information with weighting factors computed by SVDFeature.The experimental results demonstrate that our algorithm effectively improves the recommendation quality and alleviates cold starting problem resulting from both users and items.

Key words: Collaborative filtering,Similarity,Hybrid recommendation,Weighting factor,Cold start

[1] Breese J,Hecherman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence(UAI-98).1998:43-52
[2] Herlocker L J,Konstan A J,Riedl T J.Empirical analysis of design choices in neighborhood-based collaborative filtering algorithms[J].Information Retrieval,2002,5(4):287-310
[3] Zeng C,Xing C X,Zhou L Z.A personalized search algorithm by using content-based filtering[J].Journal of Software,2003,14(5):999-1004
[4] Stawar B,Karypis G,Konstan J,et al.Item-Based Collaborative Filtering Recommendation Algorithms[A]∥Proceedings of the 10th International World Wide Web Conference[C].Paris:IEEE Computer Society Press,2001:285-295
[5] Dempster A,Laird N,Rubin D.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society,1977,B39:1-38
[6] Thiesson B,Meek C,Chickering D,et al.Learning mixture of DAG models[R].Technical Report,MSR-TR-97-30.Redmond:Microsoft Research,1997
[7] Sarwar B M,Karypis G,Konstan J A,et al.Application of dimensionality reduction in recommender system—A case study[C]∥ACM WebKDD 2000Workshop.2000
[8] Peng Yu,Cheng Xiao-ping.Item-based collaborative filtering algorithm using attribute similarity[J].Computer Engineering and Applications,2007,43(14):144-147
[9] Wu Yue-ping,Zheng Jian-guo.Collaborative Filtering Recom-mendation Algorithm on improved similarity measure method[J].Computer Applications and Software,2011,28(10)
[10] Deng Ai-lin,Zhu Yang-yong,Shi Bai-le.A Collaborative Filte-ring Recommendation Algorithm Based on Item Rating Prediction[J].Journal of Software,2003,14(9):1621-1628
[11] Cheng Shu,Gui Lin,Ji Hang.Subjective Rating NormalizationAlgorithm and Error Analysis[J].Journal of Higher Correspondence Education(Natural Sciences),2007,21(5)
[12] Breese J S,Heckerman D,Kadie C.Empirical Analysis of Predictive Algorithms for Callaborative Filtering[R].Technical Report,MSR-TR-98-12.May 1998
[13] Chen Tian-qi,Zheng Zhao,Lu Qiu-xia,et al.Infomative Ensemble of Multi-Resolution Dynamic Factorization Models[C]∥KDD-Cup Workshop.2011
[14] Chen Tian-qi,Tang Lin-peng,Liu Qin,et al.Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation[C]∥KDD-Cup Workshop.2012
[15] Chen Tian-qi,Zheng Zhao,Yong Yu,et al.Svdfeature:User Reffrence Manual[R].Technical Report,APEX-TR-2011-09-17.Apex Data & Konwledge Management Lab,Shanghai Jiaotong University,2011
[16] Li Xue,Zuo Wan-li,He Feng-ling,et al.An Improved Item-Based Collaborative Filtering Recommendation Algorithm[J].Journal of Computer Research and Development,2009,46(Suppl.):394-399

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