A collaborative filtering recommendation method based on clustering and user trust

被引：0

作者：

Shi, Jiaokai ^{[1
]}

Tang, Yan ^{[1
]}

Xu, Pingan ^{[1
]}

Zhang, Huirong ^{[1
]}

机构：

[1] School of Computer and Information Science, Southwest University, Chongqing

来源：

Journal of Computational Information Systems | 2015年 / 11卷 / 18期

关键词：

Clustering; Collaborative filtering; Sparse data; User trust;

D O I：

10.12733/jcis15642

中图分类号：

学科分类号：

摘要：

Collaborative filtering recommendation technology is mainly divided into two categories, user-based and item-based. The predicted value may be inaccurate because the user rating data sparsity exist. At the same time, the rapidly increasing number of users and items can also lead to the deviation of user preference score, which would seriously affect the accuracy of the final result of the recommendation. In order to improve the recommendation accuracy, a collaborative filtering recommendation algorithm based on clustering and user trust has proposed. The algorithm consists of four steps: firstly, clustering the users; secondly, calculating the user similarity through by combining with cosine similarity and improved user trust; thirdly, searching the nearest neighbor users; finally, generating recommendation. The experimental results show that the algorithm can effectively improve the recommendation precision of system in the case of sparse data. © 2015 by Binary Information Press.

引用

页码：6845 / 6852

页数：7

共 15 条

[1]

Sarwar B., Karypis G., Et al., Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, pp. 285-295, (2001)

[2]

Linden G., Smith B., York J., Amazon. com recommendations: Item-to-item collaborative filtering, Internet Computing, IEEE, 7, 1, pp. 76-80, (2003)

[3]

Ungar L.H., Foster D.P., Clustering methods for collaborative filtering, AAAI Workshop on Recommendation Systems, (1998)

[4]

Zhang C.X., Zhang Z.K., Yu L., Et al., Information filtering via collaborative user clustering modeling, Physica A: Statistical Mechanics and its Applications, 396, pp. 195-203, (2014)

[5]

Paterek A., Improving regularized singular value decomposition for collaborative filtering, Proceedings of KDD Cup and Workshop, pp. 5-8, (2007)

[6]

Ziegler C.N., Lausen G., Analyzing correlation between trust and user similarity in online communities, Trust Management, pp. 251-265, (2004)

[7]

Massa P., Avesani P., Trust-aware collaborative filtering for recommender systems, On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, pp. 492-508, (2004)

[8]

Li Y.M., Wu C.T., Lai C.Y., A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship, Decision Support Systems, 55, 3, pp. 740-752, (2013)

[9]

Zhang L., Deng X., Et al., Collaborative Filtering Recommendation Algorithm Based on User Interest Characteristics and Item Category, Journal of Computational Information Systems, 9, 15, pp. 5973-5986, (2013)

[10]

Naeen H.M., Jalali M., Naeen A.M., A trust-aware collaborative filtering system based on weighted items for social tagging systems, Intelligent Systems (ICIS), 2014 Iranian Conference on, pp. 1-5, (2014)

← 1 2 →