1. 基于特征嵌入的去流行度偏差混合推荐算法.
- Author
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李鹏, 朱心如, and 苏忻洁
- Subjects
- *
CONVOLUTIONAL neural networks , *BAYESIAN analysis , *POPULARITY , *PROBLEM solving , *RECOMMENDER systems - Abstract
In order to solve the problem of strong popularity bias in recommendation lists generated by Bayesian personalized ranking algorithm under the condition of unbalanced data, this paper designed a hybrid recommendation algorithm based on feature embedding to remove popularity bias. Firstly, this paper used convolutional neural network to extract user and item features, determine user preferences, and fill original unbalanced data according to user preferences. Secondly, this paper embedded the user preference features extracted from convolutional neural network into the Bayesian personalized ranking algorithm for hybrid recommendation. Finally, it trained the mixed recommendation model with score filled data, and obtained the personalized ranking list without popularity bias. In order to verify the performance of the algorithm, it conducted the analysis and comparison experiments on Movielens-100 K and Movielens-1 M. And the experimental results show that the popularity bias is reduced by about 50%~60% and the accuracy is improved by about twice. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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