A collaborative filtering algorithm based on biclustering

Collaborative filtering method is based on users with similar interests to provide recommendations. At present most collaborative filtering algorithms consider the similarity among users or the similarity among items without the consideration of the similarity between users and items. In this paper, biclustering algorithms based on Bimax and Order-Preserving Submatrix are introduced to measure the similarity from both the users and the items, compared with the traditional collaborative filtering algorithm proposed by Paul Resnick (1994), the Root mean square error (RMSE) and the Mean absolute error (MAE) of the former are lower than the latter´s. The experiments implemented on the datasets Movielens (100k) and Movielens (1M) proves that the collaborative filtering algorithm based on OPSM has a comparative advantage in the recommendation system. Meanwhile the larger the dataset is, the more accurate the prediction will be.