Collaborative Filtering Recommendation Based on Shuffled Frog Leaping Fuzzy Co-Clustering Algorithm

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.