中文摘要：

针对数据稀疏性问题,提出基于蚁群聚类的项目评分预测方法.在对Web日志分析基础上将用户聚类,针对目标用户的未评分项目,找到目标用户的若干最近邻类簇,利用类簇内其他用户对目标项目的评分预测未评分项目的评分,从而达到降低数据稀疏性目的.最后,结合协同过滤思想设计了相应的推荐算法,并用从自主开发的旅游电子商务网站上收集的数据进行试验仿真.实验结果表明,与其它缓解数据稀疏性的方法相比,文中的方法显著提高了推荐精度.

英文摘要：

A new recommendation algorithm based on item rating prediction using ants clustering was designed to reduce the sparsity of user rating data.By analysis the web logs of users,ants clustering algorithm was used to cluster users.For the unrated item of target user,a number of nearest neighbor cluster was found,in order to reduce data sparsity,the ratings of other users in the cluster was used to predict the unrated item.Finally,collaborative filtering algorithm was used to provide recommendation services for uses.The web server logs of a self-developed e-commerce web site were used for simulation.The result shows that,the recommendation efficiency and accuracy of our method are both obviously superior to other method.