计算机工程与应用Issue(5)：101-107,7.DOI:10.3778/j.issn.1002-8331.1307-0288
基于PSO的协同过滤推荐算法研究
Research on collaborative filtering recommendation method based on PSO algorithm
摘要
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 proj-ect/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.关键词
推荐系统/粒子群算法/协同过滤Key words
recommended system/Particle Swarm Optimization(PSO)/collaborative filtering分类
信息技术与安全科学引用本文复制引用
陆春,洪安邦,宫剑..基于PSO的协同过滤推荐算法研究[J].计算机工程与应用,2014,(5):101-107,7.基金项目
上海市科学技术委员会科研计划项目（No.13dz1508402）。 （）
