Abstract
Although recommender system has been studied for many years, the research of social recommender system is just beginning. Plenty of information can be used in social networks to improve the performance of recommender system. However, some information is very sparse when used as features. We call this feature sparsity problem. In this paper, we aimed at solving feature sparsity problem. A new strategy was proposed to expand user features by social relationships. Experiments on two real world datasets demonstrated that our method can significantly improve the recommendation performance.
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Sun, C., Lin, L., Chen, Y., Liu, B. (2013). Expanding User Features with Social Relationships in Social Recommender Systems. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_23
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DOI: https://doi.org/10.1007/978-3-642-41644-6_23
Publisher Name: Springer, Berlin, Heidelberg
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