关键词: 推荐系统, 子群算法, 协同过滤

Abstract: Collaborative filtering is one of the most effective way in the recommended system. The forecast accuracy of recommendation algorithm depends on two key points：the extraction of the nearest neighbors and the calculation of project/user similarity. The paper extracts rated most neighbors with the current project as a nearest neighbor candidate set, and proposes a weighted cosine similarity method to calculate the project/user similarity, then optimizing the weight by the Particle Swarm Optimization（PSO） algorithm. The experimental results show that using these methods can efficiently improve the accuracy of the score predicted, and provide better recommendation results than traditional collaborative filtering algorithms.

Key words: recommended system, Particle Swarm Optimization（PSO）, collaborative filtering