earticle

영어

Matrix factorization-based approaches have proven to be efficient for recommender systems. However, due to the time complexity in composing recommendations, matrix factorization-based approaches are inefficient in dealing with large scale datasets. In this paper, we present a new similar user index-based matrix factorization approach for large scale recommender systems. Finding similar users is the most time-consuming phase in large scale recommender systems. To reduce time to find the similar users, we propose a similar user index in matrix factorization. This paper describes the index structure and algorithms. Several experiments are performed. The results show that our approach is more efficient in dealing with the large dataset as compared with matrix factorization approach without the similar user index.