Abstract
With the exponential growth of users’ population and volumes of content in micro-blog web sites, people suffer from information overload problem more and more seriously. Recommendation system is an effective way to address this issue. In this paper, we studied celebrities recommendation in micro-blog services to better guide users to follow celebrities according to their interests. First we improved the jaccard similarity measure by significant weighting to enhance neighbor selection in collaborative filtering. Second, we integrated users’ social information into the similarity model to ease the cold start problem. Third we increased the density of the rating matrix by predicting the missing ratings to ease the data sparsity problem. Experiment results show that our algorithm improves the recommendation quality significantly.
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Liu, Q., Xiong, Y., Huang, W. (2013). Integrating Social Information into Collaborative Filtering for Celebrities Recommendation. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_12
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DOI: https://doi.org/10.1007/978-3-642-36543-0_12
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