A systematic literature review of sparsity issues in recommender systems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems
Link
http://link.springer.com/content/pdf/10.1007/s13278-020-0626-2.pdf
Reference65 articles.
1. Anand D, Bharadwaj K (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst Appl 38(5):5101–5109. https://doi.org/10.1016/j.eswa.2010.09.141
2. Cai G, Chen N (2018) Constrained probabilistic matrix factorization with neural network for recommendation system. In: International conference on intelligent information processing, pp 236–246. https://doi.org/10.1007/978-3-030-00828-4_24
3. Chen C, Chang KCC, Li Q, Zheng X (2018a) Semi-supervised learning meets factorization: learning to recommend with chain graph model. ACM Trans Knowl Discov Data (TKDD) 12(6):73. https://doi.org/10.1145/3264745
4. Chen R, Hua Q, Gao Q, Xing Y (2018b) A hybrid recommender system for Gaussian mixture model and enhanced social matrix factorization technology based on multiple interests. Math Probl Eng. https://doi.org/10.1155/2018/9109647
5. Dixit VS, Jain P (2018) Recommendations with sparsity based weighted context framework. In: International conference on computational science and its applications, pp 289–305. https://doi.org/10.1007/978-3-319-95171-3_23
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