Abstract
Collaborative filtering (CF) is one of the most well-known and commonly used technology for recommender systems. However, it suffers from inherent issues such as data sparsity. Many works have been done by used additional information such as user attributes, tags and social relationships to address these problems. We proposed an algorithm named OLrs (Opinion Leaders for Recommender System) based on the trust relationships. Specifically, the opinion leaders who have a strong influence for the active user and an accurate evaluation of the recommend item will be identified. The prediction for a given item is generated by ratings of these opinion leaders and the active user. Experimental results based on Epinions data set demonstrated that the prediction accuracy of our method outperforms other approach.
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Acknowledgments
The authors are grateful to the anonymous reviewers and the helpful suggestion given by the partners. The research was supported by the National Natural Science Foundation of China (no. 61300137),the Foundation for Distinguished Young Teachers in Higher Education of Guangdong(no.Yq2014117), the Technology Project of Zhanjiang (no. 2013B01148), the Natural Science Foundation of Lingnan Normal College (no.QL1307, no.QL1410).
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Wu, D., Yang, K., Wang, T., Luo, W., Min, H., Cai, Y. (2015). Integrating Opinion Leader and User Preference for Recommendation. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_2
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DOI: https://doi.org/10.1007/978-3-319-22324-7_2
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