一种整体性的相似度计算方法  

An Integrated Method for Similarity Computation

作　　者：邓琨[1] 张尧学[2,3] 周悦芝[3] 

机构地区：[1]江西财经大学信息管理学院,南昌330013 [2]中南大学信息科学与工程学院,长沙410083 [3]清华大学计算机科学技术系,北京100084

出　　处：《情报学报》2014年第11期1133-1145,共13页Journal of the China Society for Scientific and Technical Information

基　　金：本课题得到本课题得到国家科技支撑计划（No.2012BAH13F04）;国家国际科技合作专项项目（No.2013DFB10070）;江西省自然科学基金资助项目（No.20132BAB201036）资助.

摘　　要：协同过滤是推荐系统中最流行且应用最广泛的技术。基于邻域的推荐方法作为其两种类型之一，以简单、高效、稳定和解释性强的特性被广泛应用于商业领域。相似度计算作为该方法的核心步骤，其准确性直接影响预测结果的精度。现有的相似度方法是由共同评价而不是所有评价计算得到的，反映的是局部的相似性，与实际的相似性存在偏差。评价矩阵越稀疏，偏差越大。对此，本文提出一种新的相似度计算方法JS，将整体相似度计算和原有的局部相似度计算结合，更加完整地刻画相似度，同时不增加算法的复杂度，保持其原有的简单性和高效性。并对JS进一步优化，更加细致地描述整体相似度和局部相似度的关系。实验结果表明，该方法比现有的方法更有效。Collaborative filtering is the most popular and widely implemented technique in recommender system. Neighborhood-based method is one of two general collaborative filtering algorithms, and enjoys a huge amount of popularity, due to their simplicity, their efficiency, their stability and their explanations. Similarity computation is the key step of the method, and it can impact on the prediction accuracy deeply. Traditional similarity computation method suffers from rating data sparsity problem, and can only obtain the local similarity by measuring the common ratings. Thus, this paper propose a novel similarity computation method--JS,which can be employed to compute similarities by combined local similarity and global similarity. And an improved method is proposed to optimize the relation between local similarity and global similarity. The two methods can keep the simplicity and efficiency without additional complexity. Experimental results show that the new methods outperform traditional methods or common significance weighting methods on the prediction of ratings.

关 键 词：协同过滤 推荐系统 基于邻域 相似度 预测精度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]