Abstract
Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, existing recommendation methods have difficulty in accurately obtaining user features and item features, which seriously affects recommendation system performance. To accurately model social relationships and improve recommendation quality, we use both explicit (e.g. user-item ratings, trust relationships) and implicit (e.g. social tags) social relationships to mine users’ potential interest preferences; thus, we propose a social recommendation method incorporating trust relationships and social tags. The method maps user features and item features to a shared feature space using the above social relationship, obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationships to improve recommendation performance. The experimental results demonstrate that our proposed approach achieves superior performance over the other social recommendation approaches.
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Acknowledgements
We would like to thank the anonymous reviewers and editor for their helpful comments. This work was supported in part by the National Natural Science Foundation of China under Grants 61672471, 61975187, and 61802352, in part by the Industrial Science and Technology Research Project of Henan Province under Grants 212102210410, 212102310556, 202102210387, 202102210178, 212102210418, 222102210031, 222102110045, 222102210323, 222102210030, 222102210024, and 182102310969, in part by the Zhongyuan Science and Technology Innovation Leadership Program under Grant 214200510026, in part by the Natural Science Foundation Projectin of Henan Province under Grant 222300420582, in part by the Blue Book of Science Research Report on the "Belt and Road" Tourism Development Grant 2017sz01, in part by Shaanxi innovation capability support plan under Grant 2018KRM071, in part by the Doctoral Fund Project of Zhengzhou University of Light Industry under Grants 2020BSJJ030 and 2020BSJJ031, and in part by the innovation team of data science and knowledge engineering of Zhengzhou University of Light Industry under Grant 13606000032.
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The content of this article has not been published, nor has it been submitted for consideration to other journals. There is no conflict of interest in the content of this article. With the consent of all the authors, this article will be authorized for publication. The contributions of each author in this article are as follows: Dr. Rui Chen wrote the article, Prof. Jian-wei Zhang revised the paper, Prof. Zhifeng Zhang put forward many valuable suggestions for this article, Dr. Jingli Gao verified the method and experiment, Dr. Pu Li completed the experiment of the paper, and Prof. Hui Liang revised the grammar of the paper.
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Chen, R., Zhang, Jw., Zhang, Z. et al. A comprehensive social matrix factorization for recommendations with prediction and feedback mechanisms by fusing trust relationships and social tags. Soft Comput 26, 11479–11496 (2022). https://doi.org/10.1007/s00500-022-07440-x
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DOI: https://doi.org/10.1007/s00500-022-07440-x