Aiming at the problems that the data are sparse and the results are not accurate in traditional recommendation algorithms, this paper proposes an item clustering recommendation algorithm based on Particle Swarm Optimization(PSO) algorithm. It uses PSO to engender the cluster centers, calculates the similarity between target item and cluster centers to search the nearest neighbors of target item, and gains a recommendation, so that it improves the accuracy and the real-time performance. Experimental results indicate that the algorithm can effectively improve the accuracy of the recommendation system

基于粒子群优化的项聚类推荐算法.pdf

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