摘要
基于大众分类法(folksonomy)的标签应用已逐渐成为一种重要的互联网内容组织方式,但随着数据规模的海量增长,产生了严重的信息过载问题,而传统的基于"用户-项目"二元关系的个性化推荐算法难以有效应对由"用户-项目-标签"所构成的三元关系。通过对基本人工鱼群算法进行改进,提出一种对标签推荐系统初始数据集进行聚类分析的方法,用以降低标签推荐系统的数据分析规模。在此基础上,综合考虑标签推荐系统中的元素权重以及反映用户偏好的评分信息,将元素权重和评分等级进行加权处理,以处理结果作为张量中的元素,建立了一种新的加权张量模型,并利用动态增量更新的张量分解算法进行模型求解,进而完成个性化的推荐。最后在两个真实的实验数据集上对比分析了所提算法(FTA)与另外两个经典标签推荐算法的推荐性能,实验结果表明FTA算法在准确率和召回率上均具有较好的表现。
Popular classification （Folksonomy） tag application has gradually become an important way of internet content organization, but with the massive increase in the scale of data, the problem of information overload has been produced. On the other hand, the traditional personalized recommendation algorithm based on the relationship between ＇user item＇ is difficult to have effect on the three elements of the ＂user-item-label＂. Based on the improvement of basic artificial fish swarm algorithm, a clustering analysis method was proposed for the initial data set of the tag recommendation system（TRS） ,which is used to reduce the scale of the data analysis of the TRS. Based on this, through comprehensive consideration of the label recommendation system element weights and the reflection of user preference score information, and by weighted processing of the element weights and grades as the elements in the tensor, a new weighted tensor model was established, and the model was solved by the dynamic incremental updating of the tensor decomposition algorithm, completing the personalized recommendation. Finally, on two real experimental data sets, the proposed algorithm （FTA） and the other two classic tag recommendation algorithms were compared and analyzed. The experimental results show that the FTA algorithm has better performance in the recall rate and precision rate.
出处
《计算机科学》
CSCD
北大核心
2016年第12期168-172,共5页
Computer Science
基金
江苏省高校哲学社会科学项目(2014SJB688)
国家统计局项目(2014LY058)
教育部人文社会科学项目(13YJCZH077)资助
关键词
鱼群算法
聚类分析
张量分解
标签推荐
Artificial fish swarm algorithm,Clustering analysis,Tensor decomposition,Tag recommendation
作者简介
张浩（1982-），男，博士生，主要研究方向为计算机应用、交通大数据分析，E-mail：andyhao@seu．edu．cn；
何杰（1973-），男，博士，教授，主要研究方向为交通安全与物流规划；
李慧宗（1979-），男，博士，副教授，主要研究方向为社会化推荐系统。
