Abstract
Abstract There are many good collaborative recommendation methods, it is still challenging the user preferences to meet to increase the accuracy and validity of these methods. It is novel collaborative filtering based on the algorithm K-means. In clustering we use the Bee Colony (BC) algorithm to overcome the problem of local optimization by the K-means method. The improved cosine similarity to calculate the similarity between users same cluster. Finally, we generate the corresponding target user recommendation results. Benchmark datasets and real-world datasets, based on user clustering algorithms, show that our collaborative filtering method is a more detailed numerical analysis than many other proposed methods.
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