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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 90-99    DOI: 10.11925/infotech.2096-3467.2017.07.11
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Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users
Xue Fuliang(), Liu Junling
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
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Abstract  

[Objective] This paper tries to improve user similarity calculation in collaborative filtering recommendation with trust relationship among them. Once there is no similar user for members of the target group, we recommend the most trusted ones as the similar users. [Methods] First, we retrieved the trusted users as candidates for the similar users. Second, we combined the trusted and the target users to form a project score set, and evaluated the estimated value of the projects receiving no comment from the target group. Third, we quantified the trust relationship among users to form a regulation factor. Finally, we calculated the adjustment factor and created the similarity cluster of users, and made cross-recommendation among similar users. [Results] The collaborative filtering recommendation method based on trust relationship had better performance than traditional ones. [Limitations] Only examined the new method with one sample dataset with trusted relationship. More research is needed to test the proposed method with other datasets. [Conclusions] The trusted relationship among users contains valuable information, which could be used to calculate user similarity for collaborative filtering recommendation services, and then effectively solves the sparsity and cold start issue.

Key wordsE-commerce Recommendation      User Trust      Collaborative Filtering      Cold Start      Sparsity     
Received: 26 May 2017      Published: 13 September 2017
ZTFLH:  TP301.6  

Cite this article:

Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users. Data Analysis and Knowledge Discovery, 2017, 1(7): 90-99.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.07.11     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I7/90

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指标 CF CCF ECF
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[1] Wang Y, Singh M P.Formal Trust Model for Multiagent System[C]//Proceedings of the 20th International Joint Conference on Artificial Intelligence.2007: 1551-1556.
[2] Lampropoulos A S, Lampropoulos P S, Tsihrintzis G A.A Cascade-Hybrid Music Recommender System for Mobile Services Based on Musical Genre Classification and Personality Diagnosis[J]. Multimedia Tools and Applications, 2012, 59(1): 241-258.
doi: 10.1007/s11042-011-0742-0
[3] Shambour Q, Lu J.A Trust-semantic Fusion-based Recommendation Approach for E-business Application[J]. Decision Support Systems, 2012, 54(1): 768-780.
doi: 10.1016/j.dss.2012.09.005
[4] Adomavicius G, Tuzhilin A.Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
doi: 10.1109/TKDE.2005.99
[5] Jøsang A, Quattrociocchi W, Karabeg D.Taste and Trust[C]//Proceedings of IFIP International Conference on Trust Management.2011: 312-322.
[6] Chowdhury M, Thomo A, Wadge W W.Trust-based Infinitesimals for Enhanced Collaborative Filtering[C]// Proceedings of the 15th International Conference on Management of Data. 2009.
[7] Danis C, Singer D.A Wiki Instance in the Enterprise: Opportunities, Concerns and Reality[C]//Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work.2008: 495-504.
[8] De Rosa C, Cantrell J, Havens A, et al.Sharing, Privacy and Trust in Our Networked World[R]. A Report to the OCLC Membership, OCLC, 2007.
[9] Golbeck J A.Computing and Applying Trust in Web-Based Social Networks[D]. College Park, MD, USA: University of Maryland at College Park, 2005.
[10] 余力, 刘鲁. 电子商务个性化推荐研究[J]. 计算机集成制造系统, 2004, 10(10): 1306-1313.
doi: 10.3969/j.issn.1006-5911.2004.10.025
[10] (Yu Li, Liu Lu.Research on Personalized Recommendations in E-business[J]. Computer Integrated Manufacturing Systems, 2004, 10(10): 1306-1313.)
doi: 10.3969/j.issn.1006-5911.2004.10.025
[11] 刘建国, 周涛, 汪秉宏. 个性化推荐系统的研究进展[J]. 自然科学进展, 2009, 19(1): 1-15.
doi: 10.3321/j.issn:1002-008X.2009.01.001
[11] (Liu Jianguo, Zhou Tao, Wang Binghong.Study on the Research of Personalized Recommendation System[J]. Progress in Natural Science, 2009, 19(1): 1-15.)
doi: 10.3321/j.issn:1002-008X.2009.01.001
[12] 邓爱林, 朱扬勇, 施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003, 14(9): 1621-1628.
[12] (Deng Ailin, Zhu Yangyong, Shi Bole.A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction[J]. Journal of Software, 2003, 14(9): 1621-1628.)
[13] 龙宇, 童向荣. 结合信任的推荐系统的性质[J]. 计算机应用, 2014, 34(1): 222-226, 235.
doi: 10.11772/j.issn.1001-9081.2014.01.0222
[13] (Long Yu, Tong Xiangrong.Property of Trust-based Recommendation System[J]. Journal of Computer Applications, 2014, 34(1): 222-226, 235.)
doi: 10.11772/j.issn.1001-9081.2014.01.0222
[14] 邹本友, 李翠平, 谭力文, 等. 基于用户信任和张量分解的社会网络推荐[J]. 软件学报, 2014, 25(12): 2852-2864.
doi: 10.13328/j.cnki.jos.004725
[14] (Zou Benyou, Li Cuiping, Tan Liwen, et al.Social Recommendations Based on User Trust and Tensor Factorization[J]. Journal of Software, 2014, 25(12): 2852-2864.)
doi: 10.13328/j.cnki.jos.004725
[15] Guo G, Zhang J, Thalmann D.Merging Trust in Collaborative Filtering to Alleviate Data Sparsity and Cold Start[J]. Knowledge-Based Systems, 2014, 57: 57-68.
doi: 10.1016/j.knosys.2013.12.007
[16] Ma X, Lu H, Gan Z, et al.An Explicit Trust and Distrust Clustering Based Collaborative Filtering Recommendation Approach[J]. Electronic Commerce Research and Applications, 2017, 25: 29-39.
doi: 10.1016/j.elerap.2017.06.005
[17] Jia D, Zhang F, Liu S.A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model[J]. Journal of Software, 2013, 8(1): 11-18.
doi: 10.4304/jsw.8.1.11-18
[18] Xu X L, Xu G L.Improved Collaborative Filtering Recommendation Based on Classification and User Trust[J]. Journal of Electronic Science and Technology, 2016, 14(1): 25-31.
doi: 10.11989/JEST.1674-862X.504071
[19] Du Y, Du X, Huang L.Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction[J]. Chinese Journal of Electronics, 2016, 25(3): 418-423.
doi: 10.1049/cje.2016.05.005
[20] Jamali M, Ester M.Trustwalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation[C]// Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009: 397-406.
[21] Diaz-Aviles E, Drumond L, Schmidt-Thieme L, et al.Real-time Top-n Recommendation in Social Streams[C]// Proceedings of the 6th ACM Conference on Recommender Systems.2012: 59-66.
[22] 陈宇亮, 沈奎林. 基于读者评论的图书推荐系统研究[J]. 图书情报导刊, 2016, 1(9): 6-9.
[22] (Chen Yuliang, Shen Kuilin.Study on Book Recommendation System Based on Reader’s Comments[J]. Journal of Library and Information Science, 2016, 1(9): 6-9.)
[23] 李琦. 基于社交网络好友信任度的个性化推荐系统研究[D]. 哈尔滨: 哈尔滨工业大学, 2014.
[23] (Li Qi.Study on Personalized Recommendation System Based on Social Network Friends’ Trust [D]. Harbin: Harbin Institute of Technology, 2014.)
[24] 肖志宇. 基于社交网络和信任模型的推荐系统的研究与实现[D]. 南京: 东南大学, 2015.
[24] (Xiao Zhiyu.Research and Implementation of Recommendation System Based on Social Network and Trust Model [D]. Nanjing: Southeast University, 2015.)
[25] 孙国豪. 社交网络中基于信任的推荐系统[D]. 苏州: 苏州大学, 2015.
[25] (Sun Guohao.Trust-based Recommendation System in Social Network [D]. Suzhou: Soochow University, 2015.)
[26] 朱岩, 林泽楠. 电子商务中的个性化推荐方法评述[J]. 中国软科学, 2009(2): 183-192.
doi: 10.3969/j.issn.1002-9753.2009.02.022
[26] (Zhu Yan, Lin Zenan.A Review of E-Business Recommendation System[J]. Chinese Soft Science, 2009(2): 183-192.)
doi: 10.3969/j.issn.1002-9753.2009.02.022
[27] 李聪, 梁昌勇, 董珂. 基于项目类别相似性的协同过滤推荐算法[J]. 合肥工业大学学报: 自然科学版, 2008, 31(3): 360-363.
[27] (Li Cong, Liang Changyong, Dong Ke.A Collaborative Filtering Recommendation Algorithm Based on Item Category Similarity[J]. Journal of Hefei University of Technology: Natural Science Edition, 2008, 31(3): 360-363.)
[28] 李晓昀, 阳小华, 余颖. 基于隐性反馈分析的个性化推荐研究[J]. 计算机工程与设计, 2009, 30(16): 3794-3796, 3825.
[28] (Li Xiaoyun, Yang Xiaohua, Yu Ying.Research on Individualized Recommendation Based on Implicit Feedback Analyses[J]. Computer Engineering and Design, 2009, 30(16): 3794-3796, 3825.)
[29] 余力, 刘鲁, 李雪峰. 用户多兴趣下的个性化推荐算法研究[J]. 计算机集成制造系统, 2004, 10(12): 1610-1615.
doi: 10.3969/j.issn.1006-5911.2004.12.026
[29] (Yu Li, Liu Lu, Li Xuefeng.Research on Personalized Recommendation Algorithm for Users Multiple-interests[J]. Computer Integrated Manufacturing Systems, 2004, 10(12): 1610-1615.)
doi: 10.3969/j.issn.1006-5911.2004.12.026
[30] 孙小华. 协同过滤系统的稀疏性与冷启动问题研究[D]. 杭州: 浙江大学, 2005.
[30] (Sun Xiaohua.Study on Sparsity and Cold Start of Collaborative Filtering System [D]. Hangzhou: Zhejiang University, 2005.)
[31] 许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2): 350-362.
doi: 10.3724/SP.J.1001.2009.03388
[31] (Xu Hailing, Wu Xiao, Li Xiaodong, et al.Comparison Study of Internet Recommendation System[J]. Journal of Software, 2009, 20(2): 350-362.)
doi: 10.3724/SP.J.1001.2009.03388
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