doi: 10.4304/jsw.6.4.732-739
A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map
Abstract—With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using itembased collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.
Index Terms—personalized service, recommender systems, association rules mining, collaborative filtering, mean absolute error
Cite: Hongwu Ye, "A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map," Journal of Software vol. 6, no. 4, pp. 732-739, 2011.
General Information
ISSN: 1796-217X (Online)
Abbreviated Title: J. Softw.
Frequency: Quarterly
APC: 500USD
DOI: 10.17706/JSW
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Cecilia Xie
Abstracting/ Indexing: DBLP, EBSCO,
CNKI, Google Scholar, ProQuest,
INSPEC(IET), ULRICH's Periodicals
Directory, WorldCat, etcE-mail: jsweditorialoffice@gmail.com
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