文章基本信息

标题：Effective Constraint based Clustering Approach for Collaborative Filtering Recommendation using Social Network Analysis
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作者：S. Kanimozhi
期刊名称：Bonfring International Journal of Data Mining
印刷版ISSN：2250-107X
电子版ISSN：2277-5048
出版年度：2011
卷号：1
期号：Inaugural Special Issue
页码：12-17
DOI：10.9756/BIJDM.I1003
语种：English
出版社：Bonfring
摘要：Recommender system helps people to find information or items that they needed. Collaborative Filtering (CF) is an eminent technique in recommender systems. CF uses relationships between users and recommends items to the active user based on the ratings of his/her neighbors. But, there are several drawbacks in CF like data sparsity problem, where users only rate a small set of items. This drawback makes the computation of similarity between users imprecise and consequently minimizes the accuracy of CF algorithms. Recently, clustering is bound to produce very good accurate results in the recommendation results. In this paper, a constraint based clustering approach depending on the social information of users to obtain the recommendations is proposed. The application of this approach is studied in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library, the evaluation shows that the proposed constrained clustering technique based CF performs better than clustering based CF algorithms.
关键词：Recommender System; Collaborative Filtering; Clustering; Constraints