| Summary: | The collaborative filtering algorithm is widely used in the field of recommendation because of its good recommendation effect,which nonetheless is significantly reduced when the data is sparse and from a cold start.In this case,in order to make full use of the user’s historical information to improve the recommendation precision,this paper proposes an improved clustering joint similarity recommendation algorithm.The center point of K-means++clustering is improved by using the bee colony algorithm,so that the cluster center in the whole data is optimal,and the clustering results are integrated to further optimize the clustering.According to the clustering results,the improved user similarity algorithm is used to optimize the traditional similarity algorithm in the same class,so that the similarity between users is optimal.Then the optimal results are recommended to users according to the score prediction method in the field.Experimental results show that the proposed algorithm outperforms other existing algorithms in terms of the precision,recall rate and Mean Absolute Error(MAE),and its performance is still the best in the case of sparse data.
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