Abstract
This paper presents a method for overcoming sparsity problem of collaborative filtering system. The proposed method is based on an intuition on a network of human (such as a friendship network with friends of a friend). This method increases the density of similarity matrix and the coverage of predictions. We use sparse training data to test the sparsity of real-world situation. Consequently, experimental results show that this method increases coverage and f-measure especially for sparse training data.
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Lee, S., Yang, J., Park, SY. (2004). Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_36
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DOI: https://doi.org/10.1007/978-3-540-30214-8_36
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