Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (6): 27-32    DOI: 10.11925/infotech.1003-3513.2015.06.05
Current Issue | Archive | Adv Search |
A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction
Ying Yan, Cao Yan, Mu Xiangwei
Transportation Management College, Dalian Maritime University, Dalian 116000, China
Download: PDF(521 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] By improving the traditional collaborative filtering recommendation algorithm to alleviate the existing data sparseness problem, thus enhance the prediction precision. [Methods] This paper proposes a hybrid collaborative filtering recommender framework and KSUBCF algorithm integrated K-means clustering and Slope One algorithm. Firstly, this algorithm uses the Slope One algorithm based on K-means clustering to predict item default rating. And then, to implement recommendation by the collaborative filtering recommendation algorithm based on users. [Results] The experimental results show that with the increase of neighbors numbers, this algorithm is better than the original Slope One algorithm, which MAE value is reduced by 8.8% to 21% and RMSE value is reduced by 17% to 28.1%. [Limitations] This algorithm still relies on user-project score data matrix. [Conclusions] Compared with other traditional collaborative filtering algorithms, the decreases of the MAE value are 10% and 43.8% respectively and the decreases of the RMSE value are 20.1% and 37.4%. The proposed method can improve the prediction precision.

Key wordsHybrid collaborative filtering      Item rating      Slope One prediction      MAE     
Received: 12 December 2014      Published: 08 July 2015
:  G202  

Cite this article:

Ying Yan, Cao Yan, Mu Xiangwei. A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction. New Technology of Library and Information Service, 2015, 31(6): 27-32.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.06.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I6/27

[1] 李聪, 梁昌勇, 杨善林. 电子商务协同过滤稀疏性研究: 一个分类视角[J]. 管理工程学报, 2011, 25(1): 94-101. (Li Cong, Liang Changyong, Yang Shanlin. Sparsity Problem in Collaborative Filtering: A Classification [J]. Journal of Industrial Engineering and Engineering Management, 2011, 25(1): 94-101.)
[2] Huang Z, Chen H, Zeng D. Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering [J]. ACM Transactions on Information Systems, 2004, 22(1): 116-142.
[3] 王洋, 骆力明. 一种解决协同过滤数据稀疏性问题的方法[J]. 首都师范大学学报: 自然科学版, 2012, 33(4): 1-5, 26. (Wang Yang, Luo Liming. In Collaborative Filtering a Method of Alleviating the Sparsity Problem [J]. Journal of Capital Normal University: Natural Science Edition, 2012, 33(4): 1-5,26.)
[4] Zhang J Y, Pu P. A Recursive Prediction Algorithm for Collaborative Filtering Recommender Systems [C]. In: Proceedings of the 2007 ACM Conference on Recommender systems, Minneapolis, MN, USA. 2007: 57-64.
[5] 林德军. 基于Slope One改进算法推荐模型的设计与实现[D]. 北京: 北京邮电大学, 2012. (Lin Dejun. Design and Realization of the Recommendation Model Based on the Slope One Improved Algorithm [D]. Beijing: Beijing University of Posts and Telecommunications, 2012.)
[6] 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. (Deng Ailin, Zhu Yangyong, Shi Baile. A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction [J]. Journal of Software, 2003, 14(9): 1621-1628.)
[7] 王鹏, 王晶晶, 俞能海. 基于核方法的User-Based协同过滤推荐算法[J]. 计算机研究与发展, 2013, 50(7): 1444-1451. (Wang Peng, Wang Jingjing, Yu Nenghai. A Kernel and User-Based Collaborative Filtering Recommen­dation Algorithm [J]. Journal of Computer Research and Development, 2013, 50(7): 1444-1451.)
[8] Ma H, King I, Lyu M R. Effective Missing Data Prediction for Collaborative Filtering [C]. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007: 39-46.
[9] Lemire D, Maclachlan A. Slope One Predictors for Online Rating-Based Collaborative Filtering [C]. In: Proceedings of the 2005 SIAM International Conference on Data Mining (SDM'05), Newport Beach,California, USA. 2005.
[10] 孙丽梅, 李晶皎, 孙焕良. 基于动态k近邻的SlopeOne协同过滤推荐算法[J]. 计算机科学与探索, 2011, 5(9): 857-864. (Sun Limei, Li Jingjiao, Sun Huanliang. SlopeOne Collaborative Filtering Recommendation Algorithm Based on Dynamic k-Nearest-Neighborhood [J]. Journal of Frontiers of Computer Science and Technology, 2011, 5(9): 857-864.)
[11] Zhang D J. An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing [C]. In: Proceedings of the 2nd International Symposium on Electronic Commerce and Security, Nanchang, China. IEEE, 2009: 215-217.
[12] Wang P, Ye H W. A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering [C]. In: Proceedings of the 2009 International Conference on Industrial and Information Systems, Haikou, China. IEEE, 2009: 152-154.
[13] 肖敏. 基于领域本体的电子商务推荐技术研究[D]. 武汉: 武汉理工大学, 2009. (Xiao Min. Research on Electronic Commerence Recommendation Technology Based on Domain Ontology [D]. Wuhan: Wuhan University of Technology, 2009.)
[14] Chen Y J, Chu H C, Chen Y M, et al. Adapting Domain Ontology for Personalized Knowledge Search and Recommendation [J]. Information & Management, 2013, 50(6): 285-303.
[15] Cheng S T, Chou C L, Horng G J. The Adaptive Ontology- based Personalized Recommender System [J]. Wireless Personal Communications, 2013, 72(4): 1801-1826.

[1] Song Meiqing. Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation[J]. 现代图书情报技术, 2015, 31(12): 28-33.
[2] Wang Zhongqun, Le Yuan, Xiu Yu, Huang Subin, Wang Qiansong. Collusive Sales Fraud Detection Based on Users' Information Search Behavior Template and Statistical Analysis[J]. 现代图书情报技术, 2015, 31(11): 41-50.
[3] He Yue, Song Lingxi, Qi Liyun. Spillover Effect of Internet Word of Mouth in Negative Events——Take the “Deadly Yuantong Express” Event for an Example[J]. 现代图书情报技术, 2015, 31(10): 58-64.
[4] Zhang Liyi, Zhang Jiao. A Brusher Detection Method Based on Principle Component Analysis and Random Forest[J]. 现代图书情报技术, 2015, 31(10): 65-71.
[5] Wang Zhongqun, Huang Subin, Xiu Yu, Zhang Yi. Research on Metrics-Model for Online Product Review Depth Based on Domain Expert and Feature Concept Tree of Products[J]. 现代图书情报技术, 2015, 31(9): 17-25.
[6] Zhao Jingxian. Detect of Internet Fake Public Opinion Based on Decision Tree[J]. 现代图书情报技术, 2015, 31(6): 78-84.
[7] Wu Jiehua, Zhu Anqing. Mixture Topological Factors for Collaboration Prediction in Academic Network[J]. 现代图书情报技术, 2015, 31(4): 65-71.
[8] Li Sheng, Wang Yemao. An Ontology-based and Location-aware Book Recommendation Model in Library[J]. 现代图书情报技术, 2015, 31(3): 58-66.
[9] Chen Tao, Zhang Yongjuan, Chen Heng. Implementation of the Framework for Converting Web-data to RDF (W2R)[J]. 现代图书情报技术, 2015, 31(2): 1-6.
[10] Wang Weijun, Song Meiqing. A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences[J]. 现代图书情报技术, 2014, 30(6): 25-32.
[11] Wu Shanyan, Xu Xin. Cooking Recipe Recommendation System Based on CBR[J]. 现代图书情报技术, 2013, (12): 34-41.
[12] Liu Kan, Zhu Huaiping, Liu Xiuqin. Detection of Internet Deceptive Opinion Based on SVM[J]. 现代图书情报技术, 2013, 29(11): 75-80.
[13] Xiong Tao, He Yue. The Identification and Analysis of Micro-blogging Opinion Leaders in the Network of Retweet Relationship[J]. 现代图书情报技术, 2013, (6): 55-62.
[14] Li Shuqing, Wang Jianqiang. A Visualization and Recognition Method of Readers’ Interests with the Analysis of the Characteristics of Borrowing Time[J]. 现代图书情报技术, 2013, (5): 46-53.
[15] Kou Jihong, Dai Yishu, Liu Fang, Wu Jun, Xu Chenghuan, Cao Qian. The Analysis on Functional Mechanism of TheBrain[J]. 现代图书情报技术, 2012, (12): 45-51.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn