Please wait a minute...
New Technology of Library and Information Service  2014, Vol. 30 Issue (2): 41-47    DOI: 10.11925/infotech.1003-3513.2014.02.06
Current Issue | Archive | Adv Search |
An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures
Jiang Shuhao1, Xue Fuliang2
1. Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China;
2. Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] The authors improvecontent-based recommendation method through Fuzzy similarity-based collaborative filtering prediction and diversity selection algorithm to raise the recommendation quality. [Context] There are many successful applications of Content Based Recommender Systems (CB-RS).Recommendation diversity, representation of items as well as users' preference modeling are still critical parts in this field. [Methods] An effective collaborative Content-Based Filtering (CBF) is developed by introducing an item representation scheme, and measuring similarity based on the scheme, and fuzzy similarity measure and fuzzy-CF into the fuzzy-CBF with diversity, in order to improve content-based recommendation method. [Results] Experiment results show that the proposed hybrid scheme (fuzzy CF-CBF) is better than the other three popular schemes in Mean Absolute Error(MAE), coverage and diversity. [Conclusions] The proposed scheme improves the recommendation quality, while enhances the recommended diversity.

Key wordsRecommender system      Recommendation diversity      Fuzzy CF-CBF      Fuzzy similarity measures     
Received: 09 October 2013      Published: 06 March 2014
:  TP301.6  

Cite this article:

Jiang Shuhao, Xue Fuliang. An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures. New Technology of Library and Information Service, 2014, 30(2): 41-47.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.02.06     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I2/41

[1] 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.
[2] 曾春, 邢春晓, 周立柱. 基于内容过滤的个性化搜索算法[J]. 软件学报, 2003, 14(5): 999-1004. (Zeng Chun, Xing Chunxiao, Zhou Lizhu. A Personalized Search Algorithm by Using Content-Based Filtering[J]. Journal of Software, 2003, 14(5): 999-1004.)
[3] Zenebe A, Norcio A F. Representation, Similarity Measures and Aggregation Methods Using Fuzzy Sets for Content –Based Recommender Systems[J]. Fuzzy Sets and Systems, 2009, 160(1): 76-94.
[4] 黄洪, 杨卓俊, 王奔. 模糊逻辑在电子商务商品推荐系统中的应用[J]. 计算机系统应用, 2012, 21(3): 171-175. (Huang Hong, Yang Zhuojun, Wang Ben. Application of Fuzzy Logic to E-commerce Recommendation System of Commodity[J]. Computer Systems & Applications, 2012, 21(3): 171-175.)
[5] Shih Y Y, Liu D R. Product Recommendation Approaches: Collaborative Filtering via Customer Life Value and Customer Demands[J]. Expert Systems with Applications, 2008, 35(1-2): 350-360.
[6] Kant V, Bharadwaj K K. Incorporating Fuzzy Trust in Collaborative Filtering Based Recommender Systems[C]. In: Proceedingsof the 2nd International Conference of SEMCCO. Berlin, Heidelberg: Springer-Verlag, 2011: 433-440.
[7] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163-175. (Zhu Yuxiao, Lv Linyuan. Evaluation Metrics for Recommender Systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.)
[8] Balabanovi? M, Shoham Y. Fab: Content-based, Collabor-ative Recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72.
[9] 李华, 张宇, 孙俊华. 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012, 39(12): 83-86. (Li Hua, Zhang Yu, Sun Junhua. Research on Collaborative Filtering Recom-mendation Based on User Fuzzy Clustering[J]. Computer Science, 2012, 39(12): 83-86.)
[10] 王明佳, 韩景倜, 韩松乔. 基于模糊聚类的协同过滤算法[J]. 计算机工程, 2012, 38(24): 50-52. (Wang Mingjia, Han Jingti, Han Songqiao. Collaborative Filtering Algorithm Based on Fuzzy Clustering[J]. Computer Engineering, 2012, 38(24): 50-52.)
[11] 张富国, 徐升华. 基于信任的电子商务推荐多样性研究[J]. 情报学报, 2010, 29(2): 350-356. (Zhang Fuguo, Xu Shenghua. Research on Recommendation Diversification in Trust Based E-commerce Recommendation Systems[J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(2): 350-356.)
[12] 牟向伟, 陈燕. 基于模糊描述逻辑的个性化推荐系统建模[J]. 计算机应用研究, 2011, 28(4): 1429-1433. (Mu Xiangwei, Chen Yan. Fuzzy Semantic Personalized Recom-mendation System Modeling[J]. Application Research of Computers, 2011, 28(4): 1429-1433.)
[13] 严冬梅, 鲁城华. 基于用户兴趣度和特征的优化协同过滤推荐[J]. 计算机应用研究, 2012, 29(2): 497-501. (Yan Dongmei, Lu Chenghua. Optimized Collaborative Filtering Recommendation Based on User' Interest Degree and Feature[J]. Application Research of Computers, 2012, 29(2): 497-501.)
[14] 张慧颖, 薛福亮. 一种利用Vague集理论改进的协同过滤推荐算法[J]. 现代图书情报技术, 2012(3): 35-39. (Zhang Huiying, Xue Fuliang. A Collaborative Filtering Recomm-endation Algorithm Based on Vague Sets Rating Prediction[J]. New Technology of Library and Information Service, 2012(3): 35-39.)
[15] 熊忠阳, 刘芹, 张玉芳, 等. 基于项目分类的协同过滤改进算法[J]. 计算机应用研究, 2012, 29(2): 493-496. (Xiong Zhongyang, Liu Qin, Zhang Yufang, et al. Improved Algorithm of Collaborative Filtering Based on Item Classification[J]. Application Research of Computers, 2012, 29(2): 493-496.)
[16] Albadvi A, Shahbazi M. Integrating Rating-based Collabora-tive Filtering with Customer Lifetime Value: New Product Recommendation Technique[J]. Intelligent Data Analysis, 2010, 14(4): 143-155.
[17] Cho Y H, Kim J K. Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-commerce[J]. Expert Systems with Applications, 2004, 26(2): 233-246.

[1] Yu Shuo,Hayat Dino Bedru,Chu Xinbei,Yuan Yuyuan,Wan Liangtian,Xia Feng. Understanding Serendipity in Science: A Survey[J]. 数据分析与知识发现, 2021, 5(1): 16-35.
[2] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[3] 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.
[4] Liu Dan. Personalized Book Recommender Service Deployment Using Apache Mahout[J]. 现代图书情报技术, 2015, 31(10): 102-108.
[5] Tan Xueqing, He Shan. Research Review on Music Personalized Recommendation System[J]. 现代图书情报技术, 2014, 30(9): 22-32.
[6] Xue Fuliang, Zhang Huiying. A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values[J]. 现代图书情报技术, 2014, 30(7): 56-63.
[7] Hu Xinming, Luo Jianjun, Xia Huosong. Research on Interactive Recommender System Based on Commodity Domain Knowledge[J]. 现代图书情报技术, 2014, 30(10): 56-62.
[8] Zhang Huiying, Xue Fuliang. An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory[J]. 现代图书情报技术, 2012, 28(3): 35-39.
[9] Zhang Huiying, Xue Fuliang. An Integrated Recommender Method Based on CLV and Collaborative Filtering[J]. 现代图书情报技术, 2012, 28(1): 46-52.
[10] Li Cong. Review of Scalability Problem in E-commerce Collaborative Filtering[J]. 现代图书情报技术, 2010, 26(11): 37-41.
[11] Wang Hongyu,Zhao Ying,Dang Yuewu. Design of an E-commerce Recommender System Based on Hybrid Algorithm[J]. 现代图书情报技术, 2009, 3(1): 80-85.
[12] Ma Wenfeng,Gao Fengrong,Wang Shan. On State Personal Information Service Recommender System in Digital Library[J]. 现代图书情报技术, 2003, 19(2): 16-18.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn