Abstract
Living in the “information society”, we are bombarded with information whether or not we actively seek it, collaborative filtering technology on personalized recommendation are proposed as a solution in recent years. In order to improve the accuracy of the algorithm, this paper proposed a collaborative filtering recommendation algorithm based on score classification. In view of the behavioral habits that a user tends to give the items with extreme scores which he is interested in, every rating is classified according to the rating’s extremality. In the similarity measurement, the extreme ratings are classified as high-level ratings which are assigned with higher weights, and the moderate ratings that users rate out of herd mentality are assigned with lower weights. Experiments on test dataset show that our algorithm performs better in predicting the user’s ratings than traditional algorithms.
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Acknowledgments
This work was supported by the Research Innovation Fund for College Students of Beijing University of Posts, Telecommunication and Special Found for Beijing Common Construction Project, the National Natural Science Foundation of China (Nos. 61272515, 61171102, 61671081), National Science and Technology Pillar Program (2015BAH03F02).
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Hao, J., Niu, K., Meng, Z., Huang, S., Ma, B. (2017). A Collaborative Filtering Recommendation Algorithm Based on Score Classification. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10658. Springer, Cham. https://doi.org/10.1007/978-3-319-72395-2_40
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DOI: https://doi.org/10.1007/978-3-319-72395-2_40
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