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A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning
2020
Discrete Dynamics in Nature and Society
In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits
doi:10.1155/2020/6480273
fatcat:vytpdbwaljas5lfs2lohhxejrm