Please wait a minute...
Data Analysis and Knowledge Discovery  2016, Vol. 32 Issue (12): 44-49    DOI: 10.11925/infotech.1003-3513.2016.12.06
Orginal Article Current Issue | Archive | Adv Search |
New Collaborative Filtering Algorithm Based on Relative Similarity
Shuhao Jiang1,2(),Liyi Zhang1,2,Zhixin Zhang2
1School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
2Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China
Download: PDF(463 KB)   HTML ( 40
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective]The purpose of this study is to improve the overall diversity of the recommendation results. The proposed algorithm reduces errors caused by the uneven distribution and sparsity of user rating data, and then improves the recommendation accuracy and diversity. [Methods] We first generated the relative similarity index based on the number of common ratings and individual weights. Second, we modified the similarity calculation method, and the rating prediction algorithm. The proposed model improved the aggregated diversity and maintained the recommendation accuracy, which improved the marketing effects. [Results] The aggregated diversity index increased 114, the accuracy improved 6.5% on the MovieLens data compared with results generated by the traditional cosine similarity calculation, (the rating threshold was 3.5 and number of KNN is 20). [Limitations] This method was only applicable to collaborative filtering based on the nearest neighbor, and it did not include other recommendation techniques. [Conclusions] The proposed method effectively improves the diversity and accuracy of recommendation results, which significantly improves the user experience.

Key wordsAggregate diversity      Relative similarity      Collaborative filtering     
Received: 15 August 2016      Published: 22 January 2017

Cite this article:

Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity. Data Analysis and Knowledge Discovery, 2016, 32(12): 44-49.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.12.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I12/44

[1] Adomavicius G, Kwon Y.Optimization-based Approaches for Maximizing Aggregate Recommendation Diversity[J]. Informs Journal on Computing, 2014, 26(2): 351-369.
[2] Shambour Q, Lu J.An Effective Recommender System by Unifying User and Item Trust Information for B2B Applications[J]. Journal of Computer and System Sciences, 2015, 81(7): 1110-1126.
[3] Yigit M, Bilgin B E, Karahoca A.Extended Topology Based Recommendation System for Unidirectional Social Networks[J]. Expert Systems with Applications, 2015, 42(7): 3653-3661.
[4] Adomavicius G, Kwon Y.Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(5): 896-911.
[5] Nú?ez-Valdez E R, Lovelle J M C, Martínez O S, et al. Implicit Feedback Techniques on Recommender Systems Applied to Electronic Books[J]. Computers in Human Behavior, 2012, 28(4) 1186-1193.
[6] Bradley K, Smyth B.Improving Recommendation Diversity [C]. In: Proceedings of the 12th Irish Conference on Artificial Intelligence and Cognitive Science. Maynooth, Ireland.2001.
[7] Zhang M, Hurley N.Avoiding Monotony: Improving the Diversity of Recommendation Lists [C]. In: Proceedings of the 2nd ACM Conference on Recommender Systems. ACM, 2008.
[8] Chen J, Liu Y, Hu J, et al.A Novel Framework for Improving Recommender Diversity [A]. // Behavior and Social Computing [M]. Springer International Publishing.. 2013.
[9] Aytekin T, Karakaya M ?.Clustering-based Diversity Improvement in Top-N Recommendation[J]. Journal of Intelligent Information Systems, 2014, 42(1): 1-18.
[10] Bobadilla J, Ortega F, Hernando A, et al.Recommender Systems Survey[J]. Knowledge Based Systems, 2013, 46: 109-132.
[11] Lacerda A, Ziciani N.Building User Profile to Improve User Experience in Recommender Systems [C]. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013.
[12] Park Y J.The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8): 1904-1915.
[13] Fleder D, Hosanagar K.Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity[J]. Management Science, 2009, 55(5): 697-712.
[14] 王森. 一种基于整体多样性增强的推荐算法[J]. 计算机工程与科学, 2006, 38(1): 183-187.
[14] (Wang Sen.A Recommendation Algorithm Based on Aggregate Diversity Enhancement[J]. Computer Engineering & Science, 2016, 38(1): 183-187.)
[1] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[2] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[3] Yong Wang,Yongdong Wang,Huifang Guo,Yumin Zhou. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[4] Lingfeng Hua,Gaoming Yang,Xiujun Wang. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[5] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[6] Xingxin Qin,Rongbo Wang,Xiaoxi Huang,Zhiqun Chen. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[7] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[8] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[9] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[10] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[11] Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. 现代图书情报技术, 2015, 31(9): 38-45.
[12] Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification[J]. 现代图书情报技术, 2015, 31(6): 13-19.
[13] Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering[J]. 现代图书情报技术, 2015, 31(6): 20-26.
[14] Ying Yan, Cao Yan, Mu Xiangwei. A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction[J]. 现代图书情报技术, 2015, 31(6): 27-32.
[15] Jiang Shuhao, Pan Xuhua, Xue Fuliang. An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering[J]. 现代图书情报技术, 2015, 31(5): 34-41.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn