In order to overcome the disadvantages of the traditional collaborative filtering recommendation algorithm

such as sparsity、cold start and low recommend quality

collaborative filtering recommendation based on shuffled frog leaping fuzzy co-clustering algorithm was proposed.First

co-clustering algorithm is used to simultaneously obtain user and item neighborhoods for the original score matrix

and then the results of co-clustering is used on rating matrix.Improve the shuffled frog leaping fuzzy

and the improved-shuffled frog leaping algorithm is used based on its fast global optimization ability to get the nearest neighbor set.Lastly

the final rating prediction is obtained.The experimental result show that filtering recommendation based on shuffled frog leaping fuzzy co-clustering algorithm will become more accurate

which can effectively relieve the impact of sparse data and improve the quality of recommendation.