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Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach
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Author(s): Sheng-Jhe Ke (National Sun Yat-Sen University, Taiwan)and Wei-Po Lee (National Sun Yat-sen University, Taiwan)
Copyright: 2017
Pages: 22
Source title:
Collaborative Filtering Using Data Mining and Analysis
Source Author(s)/Editor(s): Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)
DOI: 10.4018/978-1-5225-0489-4.ch011
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Abstract
Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbors for a particular user and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbors. In this chapter, we present a data analytics approach that combines user preference and social trust for making better collaborative recommendation. The proposed approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can be used to enhance the recommendation performance in an adaptive way for different datasets without an iterative parameter-tuning process.
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