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Matrix factorization completed multicontext data for tensor-enhanced recommendation.
- Source :
-
Journal of Intelligent & Fuzzy Systems . 2021, Vol. 41 Issue 6, p6727-6738. 12p. - Publication Year :
- 2021
-
Abstract
- Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATRIX decomposition
*RECOMMENDER systems
Subjects
Details
- Language :
- English
- ISSN :
- 10641246
- Volume :
- 41
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 154454901
- Full Text :
- https://doi.org/10.3233/JIFS-210641