Abstract
Based on the restricted Boltzmann machine (RBM) collaborative filtering algorithm in recommendation phase easy to weaken the needs of individual users, and the model has poor ability of anti over-fitting. In this paper, the traditional nearest neighbor algorithm is introduced into the recommendation stage of RBM, use the characteristics of interest similarity, the nearest neighbor’s interest is used as the target user’s, strengthen the individual needs of users: First, using the traditional K-mean algorithm to find out the user’s n nearest neighbors; Then, using nearest neighbor to calculate the probability of users rating grades for the non rating items; Finally, weighted average score probability to the RBM model in the process of recommendation. Using benchmark data set Movielens experimental results show that the improved RBM model with nearest neighbor can not only improve the accuracy of the model results, but also increase the ability to resist over-fitting.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Qian, X., Liu, G. (2018). Nearest-Neighbor Restricted Boltzmann Machine for Collaborative Filtering Algorithm. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_45
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DOI: https://doi.org/10.1007/978-3-319-73317-3_45
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