《计算机应用研究》|Application Research of Computers

基于TimeRBM和项目属性聚类的混合协同过滤算法

Hybrid collaborative filtering algorithm based on TimeRBM and item attribute clustering

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作者 杜丹琪,周凤
机构 贵州大学 计算机科学与技术学院,贵阳 550025
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)02-0349-05
DOI 10.3969/j.issn.1001-3695.2018.02.007
摘要 针对受限波尔茨曼机用于协同过滤算法存在的不足,忽略了用户兴趣随时间变化,同时只利用了严重稀疏的用户评分数据,首先提出一种融合了时间信息的用户RBM模型:TimeRBM模型,即在原有RBM模型中加入时间偏置项;其次提出利用项目属性信息聚类的方法进行评分预测;最后将TimeRBM模型和项目属性聚类方法得到的两种预测结果进行加权融合得到一种高效的混合算法。在基准数据集上的实验结果表明,这种混合的算法有助于提高推荐系统的预测精度。
关键词 受限波尔茨曼机;时间函数;TimeRBM;项目属性聚类
基金项目
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英文标题 Hybrid collaborative filtering algorithm based on TimeRBM and item attribute clustering
作者英文名 Du Danqi, Zhou Feng
机构英文名 CollegeofComputerScience&Technology,GuizhouUniversity,Guiyang550025,China
英文摘要 In order to overcome the disadvantages of the restricted Boltzmann machine(RBM) for collaborative filtering algorithm, which ignored the fact that user’s interests varied over time and only used the serious sparse user rating data.Firstly, this paper proposed a user-based RBM model with time information:TimeRBM, by adding the time information bias term into the existing RBM model.Secondly, it proposed a method of clustering based on the attributes of the item for rating prediction.Finally, it weighted and fused the two prediction results obtained by the TimeRBM model and the item attribute clustering, thus built an efficient hybrid algorithm.Experimental results on the benchmark dataset show that this hybrid algorithm can improve the prediction accuracy of the recommendation system.
英文关键词 RBM; time function; TimeRBM; item attribute clustering
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收稿日期 2016/10/9
修回日期 2016/12/7
页码 349-353
中图分类号 TP301
文献标志码 A