Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (6): 20-26    DOI: 10.11925/infotech.1003-3513.2015.06.04
Current Issue | Archive | Adv Search |
A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering
Gao Huming, Zhao Fengyue
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
Export: BibTeX | EndNote (RIS)      

[Objective] This paper explores a new method combining two basic recommendation algorithms to improve the recommendation accuracy of the personalized recommendation method. [Methods] The trusted neighbors can be obtained by putting forward a calculation method of the project heat to optimize the algorithm of Pearson Correlation Coefficient and establishing the interest model for the current users and its neighbors. [Results] The experiment set in MovieLens 1M movie rating data shows that the hybrid recommendation method proposed in this paper can acquire better recommendation accuracy than the exist two kinds of hybrid recommendation methods. [Limitations] The unique characteristics of the projects need to be selected by different people who may have different opinions to the number of the characteristics and their weight distribution in the interest model. [Conclusions] The hybrid recommendation method proposed in this paper improves the recommendation accuracy of the personalized recommendation.

Key wordsPersonalized recommendation      Collaborative filtering      Content filtering      Trusted neighbors      Project heat      Interest model     
Received: 22 December 2014      Published: 08 July 2015
:  TP391  

Cite this article:

Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering. New Technology of Library and Information Service, 2015, 31(6): 20-26.

URL:     OR

[1] 刘建国, 周涛, 汪秉宏. 个性化推荐系统的研究进展 [J]. 自然科学进展, 2009, 19(1): 1-15. (Liu Jianguo, Zhou Tao, Wang Binghong. Research Progress of Personalized Recom­mendation System [J]. Natural Science Progress, 2009, 19 (1): 1-15.)
[2] 温梅. 个性化推荐中基于贝叶斯网络的用户兴趣模型研究[D]. 武汉: 华中师范大学, 2013. (Wen Mei. Research on Bayesian Network Based User Interest Model in Personalized Recommendation [D]. Wuhan: Central China Normal University, 2013.)
[3] 廉涛, 马军, 王帅强, 等. LDA-CF: 一种混合协同过滤方法 [J]. 中文信息学报, 2014, 28(2): 129-135. (Lian Tao, Ma Jun, Wang Shuaiqiang, et al. A Mixture Model for Collaborative Filtering [J]. Journal of Chinese Information Processing, 2014, 28 (2): 129-135.)
[4] 王海艳, 杨文彬, 王随昌, 等. 基于可信联盟的服务推荐方法 [J]. 计算机学报, 2014, 37(2): 301-311. (Wang Haiyan, Yang Wenbin, Wang Suichang, et al. A Service Recom­mendation Method Based on Trustworthy Community [J]. Chinese Journal of Computers, 2014, 37(2): 301-311.)
[5] 许智宏, 王宝莹. 基于项目综合相似度的协同过滤算法[J].计算机应用研究, 2014, 31(2): 398-400. (Xu Zhihong, Wang Baoying. Collaborative Filtering Algorithm Based on Item Complex Similarity [J]. Application Research of Computers, 2014, 31(2): 398-400.)
[6] 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.)
[7] 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. An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory [J]. New Technology of Library and Information Service, 2012(3): 35-39.)
[8] 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.)
[9] 饶俊阳, 贾爱霞, 冯岩松, 等. 基于本体结构的新闻个性化推荐[J]. 北京大学学报: 自然科学版, 2014, 50(1): 2-7. (Rao Junyang, Jia Aixia, Feng Yansong, et al. Ontology-based News Personalized Recommendation [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50(1): 2-7.)
[10] Goossen F, Ijntema W, Frasincar F, et al. News Personaliza­tion Using the CF-IDF Semantic Recommender [C]. In: Proceedings of the 2011 International Conference on Web Intelligence, Mining and Semantics, Sogndal, Norway. 2011: 10-21.
[11] 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.)
[12] 曹毅. 基于内容和协同过滤的混合模式推荐技术研究[D]. 长沙: 中南大学, 2007. (Cao Yi. Research on a Hybrid Recommendation Model Based on Collaborative Filtering and Content Filtering [D]. Changsha: Central South University, 2007.)
[13] 李忠俊, 周启海, 帅青红. 一种基于内容和协同过滤同构化整合的推荐系统模型[J]. 计算机科学, 2009, 36(12): 142-145. (Li Zhongjun, Zhou Qihai, Shuai Qinghong. Recommender System Model Based on Isomorphic Integrated to Content-based and Collaborative Filtering [J]. Computer Science, 2009, 36(12): 142-145.)
[14] 陈天昊, 帅建梅, 朱明.一种基于协作过滤的电影推荐方法[J]. 计算机工程, 2014, 40(1): 55-58. (Chen Tianhao, Shuai Jianmei, Zhu Ming. A Film Recommendation Method Based on Collaborative Filtering [J]. Computer Engineering, 2014, 40(1): 55-58.)
[15] Jannach D, Zanker M, Felfernig A, et al. 推荐系统[M]. 蒋凡译. 北京: 人民邮电出版社, 2013: 9-86. (Jannach D, Zanker M, Felfernig A, et al. Recommendation System [M]. Translated by Jiang Fan. Beijing: People's Posts and Telecommunications Press, 2013: 9-86.)

[1] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[2] Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[3] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[4] Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[5] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[6] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[7] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[8] Ding Yong,Chen Xi,Jiang Cuiqing,Wang Zhao. Predicting Online Ratings with Network Representation Learning and XGBoost[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
[9] 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.
[10] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[11] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[12] Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[13] Hao Ding,Shuqing Li. Personalized Recommendation Based on Predictive Analysis of User’s Interests[J]. 数据分析与知识发现, 2019, 3(11): 43-51.
[14] Li Jie,Yang Fang,Xu Chenxi. 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.
[15] Wang Daoping,Jiang Zhongyang,Zhang Boqing. 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.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938