Affiliations: [a]
College of Information Sciences and Technology, School of Information Science and Engineering, Xinjiang University, Urumqi, China
| [b] Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, China
Correspondence:
[*]
Corresponding author. Jiwei Qin, Professor of the School of Information Science and Technology of Xinjiang University, Urumqi, China. E-mail: [email protected].
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.
