A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques

Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix. Compared with several major existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.