数字图书馆个性化信息推荐系统算法研究被引量：4

Study of Digital Library Personalized Information Recommendation System

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摘要粒子群优化(PSO)模仿鸟群飞行觅食行为,通过粒子追随自己找到的最好解和整个群的最好解来完成优化。信息推荐服务是数字图书馆的一项重要的功能,本文提出应用多目标粒子群优化算法对用户和项之间的相似性同时进行聚类,为用户提供最优的信息推荐服务。在MovieLens数据集的实验结果表明我们的方法能够为用户提供有用的推荐意见,其性能优于其他推荐系统方法。 Particle swarm optimization（PSO） imitating birds flocks looking for food finding food,is used to find the best solution through the particle flying and complete the optimal process.Information recommen dation service is an important task of digital library.This article applies multiple objective particle swarm optimization algorithm to cluster simultaneously the Similarity between users and itemsusers,and pro videsthe users with the best information recommendation service.Experimental results in MovieLensdata sets show that our approach can provide a useful recommendation,and hasperformance better than other methods.

作者刘飞飞

机构地区中南林业科技大学图书馆

出处《情报科学》 CSSCI 北大核心 2012年第12期1820-1823,1829,共5页 Information Science

关键词数字图书馆推荐系统粒子群优化双聚类 digital library recommendation system particle swarm optimization biclustering

分类号 G250.76 [文化科学—图书馆学]

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