Please wait a minute...
New Technology of Library and Information Service  2008, Vol. 24 Issue (11): 60-64    DOI: 10.11925/infotech.1003-3513.2008.11.12
Current Issue | Archive | Adv Search |
An Improved Item-based Collaborative Filtering Algorithm Based on Compound Weighted Rating
Ma Li
(Business College, China West Normal University, Nanchong 637002, China)
Download: PDF (393 KB)  
Export: BibTeX | EndNote (RIS)      
Abstract  

In view of the problem that recommendation quality is seriously influenced by the sparsity of user ratings,an improved Item-based collaborative filtering algorithm based on compound weighted rating is proposed. The union of user rating items is used as the basis of similarity computing among items, moreover a compound weighted rating method is proposed to compute and complete the missing values in the union of user rating items for decreasing the sparsity. The experimental results show that the new algorithm can efficiently improve recommendation quality.

Key wordsDigital library      E-commerce      Item-based collaborative filtering      Compound weighted rating     
Received: 05 August 2008      Published: 25 November 2008
ZTFLH: 

TP311

 
Corresponding Authors: Ma Li     E-mail: cnmali@yahoo.cn
About author:: Ma Li

Cite this article:

Ma Li. An Improved Item-based Collaborative Filtering Algorithm Based on Compound Weighted Rating. New Technology of Library and Information Service, 2008, 24(11): 60-64.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.11.12     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I11/60

[1] 姜丽红, 徐博艺, 张海燕. 一种协同过滤方法及其在信息推荐系统中的实现[J]. 情报学报, 2005, 24(6): 669-673.
[2] Schafer J B, Konstan J A, Riedl J. E-commerce Recommendation Applications[J]. Data Mining and Knowledge Discovery, 2001, 5(1-2): 115-153.
[3] Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms[C]. In: Proceedings of the 10th International Conference on World Wide Web. New York: ACM Press, 2001: 285-295.
[4] Linden G, Smith B, York J. Amazon.com Recommendations: Item-to-item Collaborative Filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
[5] Vozalis M G, Margaritis K G. Applying SVD on Item-based Filtering[C]. In: Proceedings of the 5th International Conference on Intelligent System Design and Applications. Washington, DC: IEEE Computer Society Press, 2005:464-469.
[6] Hu R, Lu Y. A Hybrid User and Item-based Collaborative Filtering with Smoothing on Sparse Data[C]. In: Proceedings of the 16th International Conference on Artificial Reality and Telexistence—Workshops. Washington, DC: IEEE Computer Society Press, 2006.:184-189.
[7] Ding Y, Li X. Time Weight Collaborative Filtering[C]. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2005: 485-492.
[8] 邢春晓, 高凤荣, 战思南, 等. 适应用户兴趣变化的协同过滤推荐算法[J]. 计算机研究与发展, 2007, 44(2): 296-301.
[9] 陈健, 印鉴. 基于影响集的协作过滤推荐算法[J]. 软件学报, 2007, 18(7): 1685-1694.
[10] 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. New York: ACM Press, 2007: 39-46.
[11] 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.
[12] Jin R, Si L, Zhai C, et al. CollaborativeFiltering with Decoupled Models for Preferences and Rratings[C]. In: Proceedings of the 12th International Conference on Information and Knowledge Management. New York: ACM Press, 2003: 309-316.
[13] Jin R, Chai J Y, Si L. An Automatic Weighting Scheme for Collaborative Filtering[C]: In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2004: 337-344.
[14] 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.
[15] 马丽. 基于群体兴趣偏向度的数字图书馆协同过滤技术研究[J]. 现代图书情报技术, 2007(10): 19-22.

[1] Xiaofeng Li,Jing Ma,Chi Li,Hengmin Zhu. Identifying Commodity Names Based on XGBoost Model[J]. 数据分析与知识发现, 2019, 3(7): 34-41.
[2] Yu Chuanming,Guo Yajing,Gong Yutian,Huang Manyu,Peng Hufeng. Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model[J]. 数据分析与知识发现, 2018, 2(7): 34-45.
[3] Wang Yu,Li Xiuxiu. Evaluating Business Reputation with E-Commerce Comments[J]. 数据分析与知识发现, 2017, 1(8): 59-67.
[4] Xue Fuliang,Liu Junling. 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.
[5] Zhu Peng,Zhao Xiaoxiao,Wu Wei. Factors Influencing Mobile E-commerce Consumers’ Preferences: An Empirical Study[J]. 数据分析与知识发现, 2017, 1(3): 1-9.
[6] Qi Yunfei,Zhao Yuxiang,Zhu Qinghua. Linked Data for Mobile Visual Search System of Digital Library[J]. 数据分析与知识发现, 2017, 1(1): 81-90.
[7] Hong Liang,Qian Chen,Fan Xing. Context-aware Recommendation System for Mobile Digital Libraries[J]. 现代图书情报技术, 2016, 32(7-8): 110-119.
[8] Liu Jian,Bi Qiang,Ma Zhuo. Assessment of Digital Library’s Micro-services: An Empirical Study[J]. 现代图书情报技术, 2016, 32(5): 22-29.
[9] Liu Honglian,Zhang Pengyi,Wang Jun. Multi-session Product Information Seeking Behaviors, Motivation, and Influencing Factors[J]. 现代图书情报技术, 2016, 32(4): 1-7.
[10] Yuan Xingfu, Zhang Pengyi, Wang Jun. “State-Behavior” Modeling and Its Application in Analyzing Product Information Seeking Behavior of E-commerce Websites Users[J]. 现代图书情报技术, 2015, 31(6): 93-100.
[11] Zhang Wenjun, Wang Jun, Xu Shanchuan. The Probing of E-commerce User Need States by Page Cluster Analysis ——An Empirical Study on Women's Clothes from Taobao.com[J]. 现代图书情报技术, 2015, 31(3): 67-74.
[12] Wu Wankun, Wu Qinglie, Gu Jinjiang. Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model[J]. 现代图书情报技术, 2015, 31(11): 33-40.
[13] Chen Guo, Hu Changping. Research on the Structural Features of Keyword Network of Scientific Research Areas:An Empirical Study of LIS[J]. 现代图书情报技术, 2014, 30(7): 84-91.
[14] Xiong Yongjun, Yuan Xiaoyi. Design and Implementation of Automatic Monitoring System about Library Document Database Running State[J]. 现代图书情报技术, 2014, 30(7): 127-132.
[15] Gao Jinsong, Liang Yanqi, Li Ke, Xiao Lian, Zhou Ximan. E-commerce Credit Information Service Model for Linked Data[J]. 现代图书情报技术, 2014, 30(6): 8-16.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn