Abstract
With the rapid development of the mobile Internet, the increasing user data has brought about serious information overload. Recommendation system is a more effective solution to information overload. Collaborative filtering is one of the most widely used methods in recommendation systems. The traditional collaborative filtering algorithm performs the recommendation in terms of the rating matrix to calculate the similarity. While in most applications, the ratings of the users for the item is sparse, which leads to the issues of low recommendation accuracy and cold start. In addition, traditional collaborative filtering is based on the user’s historical behavior neglecting auxiliary information of users and items. For new users, it is impossible to accurately predict the preferences. In this paper, the Stacked Denoising AutoEncoder is integrated into collaborative filtering. The ratings and auxiliary information are taken as input, and two Stacked Denoising AutoEncoder are explored to learn the implicit representation of users and items respectively. Thus the similarity between users and items can be calculated to make score prediction. In addition, the weight factor is introduced to control the proportion of the two score predictions to improve the sparsity of collaborative filtering. Experiments are done on the MovieLens dataset, where the accuracy of the proposed algorithm is proved to be significantly improved compared with several mainstream algorithms.
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Jiang, L., Song, J., Gao, T. (2020). Improved Collaborative Filtering Algorithm Based on Stacked Denoising AutoEncoders. In: You, I., Chen, HC., Leu, FY., Kotenko, I. (eds) Mobile Internet Security. MobiSec 2019. Communications in Computer and Information Science, vol 1121. Springer, Singapore. https://doi.org/10.1007/978-981-15-9609-4_12
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DOI: https://doi.org/10.1007/978-981-15-9609-4_12
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