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New Technology of Library and Information Service  2016, Vol. 32 Issue (7-8): 101-109    DOI: 10.11925/infotech.1003-3513.2016.07.13
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A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity
Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin()
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] This paper tries to improve the performance of traditional collaborative filtering and recommendation algorithm. [Methods] We used the MovieLens dataset to evaluate the proposed algorithm. First, chose datasets with sparse degree of 0.9605, which included scoring records of 1,102 users for 2,920 movies. Second, identified the optimal number of expert users and recommended weight coefficient alpha value with series of experiments. Finally, evaluated the algorithm’s performance with comparative method. [Results] The precision of the algorithm were influenced by the expert users. When the recommended weight coefficient value was 0.6, the precision of the new algorithm was better than the traditional ones. Once the propotion of expert users increased from 2% to 20%, the coverage value increased by 0.21. Thus, the new algorithm could analyze the long tail goods more effectively. [Limitations] We did not take into account the possible correlation among different categories. [Conclusions] The proposed algorithm could effectively solve the data sparsity and cold start issues, which significantly improve the performance of the recommendation system.

Key wordsPersonalized recommendation      Collaborative filtering      Domain-Expert      Similarity     
Received: 04 April 2016      Published: 29 September 2016

Cite this article:

Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity. New Technology of Library and Information Service, 2016, 32(7-8): 101-109.

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[1] J?sang A, Ismail R, Boyd C.A Survey of Trust and Reputation Systems for Online Service Provision[J]. Decision Support Systems, 2006, 43(2): 618-644.
[2] Zhang B, Huang Z, Yu J, et al.Trust Computation for Multiple Routes Recommendation in Social Network Sites[J]. Security & Communication Networks, 2005, 12(12): 159-174.
[3] 吴应良, 姚怀栋, 李成安. 一种引入间接信任关系的改进协同过滤推荐算法[J]. 现代图书情报技术, 2015(9): 38-45.
[3] (Wu Yingliang, Yao Huaidong, Li Cheng’an.An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. New Technology of Library and Information Service, 2015(9): 38-45.)
[4] Massa P, Avesani P.Trust-Aware Recommender Systems[C]. In: Proceedings of the 1st ACM Conference on Recommender Systems. 2007.
[5] Hwang C S, Chen Y P.Using Trust in Collaborative Filtering Recommendation [A]. // New Trends in Applied Artificial Intelligence[M]. Springer Berlin Heidelberg, 2007: 1052-1060.
[6] Moradi P, Ahmadian S.A Reliability-Based Recommendation Method to Improve Trust-Aware Recommender Systems[J]. Expert Systems with Applications, 2015, 42(21): 7386-7398.
[7] 俞琰, 邱广华. 融合社会网络的协同过滤推荐算法研究[J]. 现代图书情报技术, 2012(6): 54-59.
[7] (Yu Yan, Qiu Guanghua.Research on Collaborative Filtering Recommendation Algorithm by Fusing Social Network[J]. New Technology of Library and Information Service, 2012(6): 54-59. )
[8] 杜永萍, 黄亮, 何明. 融合信任计算的协同过滤推荐方法[J]. 模式识别与人工智能, 2014, 27(5): 417-425.
[8] (Du Yongping, Huang Liang, He Ming.Collaborative Filteration Recommendation Algorithm Based on Trust Computation[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(5): 417-425.)
[9] Jamali M, Ester M.TrustWalker: A Random Walk Model for Combining Trust-Based and Item-Based Recommendation [C]. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 397-406.
[10] Chen C C, Wan Y H, Chung M C, et al.An Effective Recommendation Method for Cold Start New Users Using Trust and Distrust Networks[J]. Information Sciences, 2013, 224(2): 19-36.
[11] Bedi P, Sharma R.Trust Based Recommender System Using Ant Colony for Trust Computation[J]. Expert Systems with Applications, 2012, 39(1): 1183-1190.
[12] Lai C H, Liu D R, Lin C S.Novel Personal and Group-Based Trust Models in Collaborative Filtering for Document Recommendation[J]. Information Sciences, 2013, 239(4): 31-49.
[13] 景民昌, 唐弟官, 于迎辉. 基于专家信任优先的协同过滤推荐算法[J]. 图书情报工作, 2012, 56(11): 105-108.
[13] (Jing Minchang, Tang Diguan, Yu Yinghui.A Recommending Method Based on Expert Prior Trust in Collaborative Filtering[J]. Library and Information Service, 2012, 56(11): 105-108.)
[14] Victor P, Cornelis C, De Cock M, et al.Key Figure Impact in Trust-enhanced Recommender Systems[J]. AI Communications, 2008, 21(2-3): 127-143.
[15] Billsus D, Pazzani M J.Learning Collaborative Information Filters [C] In: Proceedings of the 15th International Conference on Machine Learning. 1998.
[16] Rodgers J L, Nicewander W A.Thirteen Ways to Look at the Correlation Coefficient[J]. American Statistician, 1988, 42(1): 59-66.
[17] Breese J S, Heckerman D, Kadie C.Empirical Analysis of Predictive Algorithms for Collaborative Filtering [C]. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 2013: 43-52.
[18] Ahn H J.A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem[J]. Information Sciences, 2008, 178(1): 37-51.
[19] Shardanand U, Maes P.Social Information Filtering: Algorithms for Automating “Word of Mouth” [C]. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1995.
[20] Herlocker J L.Evaluating Collaborative Filtering Recommender Systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
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