Author:
Jiang Liang,Liu Lu,Yao Jingjing,Shi Leilei
Abstract
AbstractThe recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
U.K.–Jiangsu 20-20 World Class University Initiative Programme
U.K.–China Knowledge Economy Education Partnership
China Postdoctoral Science Foundation
Postgraduate Research and Practice Innovation Program of Jiangsu Province
Natural Science Research Projects of Jiangsu Higher Education Institutions
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Cited by
16 articles.
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