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Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
2018
Computer and Information Science
Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance
doi:10.5539/cis.v11n2p1
fatcat:vyyrbt7exba2bhufdwoyrad3fa