基于用户联合相似度的推荐算法  

Recommendation Algorithm Based on Combined Similarity of Users

作　　者：朱振国[1] 刘民康 赵凯旋 ZHU Zhen-Guo;LIU Min-Kang;ZHAO Kai-Xuan(School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, Chin)

机构地区：[1]重庆交通大学信息科学与工程学院,重庆400074

出　　处：《计算机系统应用》2018年第5期126-132,共7页Computer Systems & Applications

摘　　要：基于用户的协同过滤推荐算法在进行近邻用户的筛选时以用户之间相似度的计算结果作为依据,数据量的增大加剧了数据的稀疏程度,导致了计算结果的准确性较差,影响了推荐准确度.针对该问题本文提出了一种基于用户联合相似度的推荐算法.用户联合相似度的计算分为用户对项目属性偏好的相似度和用户之间人口统计学信息的相似度两个部分.用户的项目属性偏好引入了LDA模型来计算,计算时评分数据仅作为筛选依据,因而避免了对数据的直接使用,减缓了稀疏数据对相似度计算结果的影响;用户之间人口统计学信息的相似度则在数值化人口统计学信息之后通过海明距离进行度量.实验结果表明,本文提出的算法在推荐准确度上优于传统协同过滤推荐算法.The user-based collaborative filtering recommendation algorithm is based on the calculation of the similarity between users when the neighbor user is screened, and the increase in the amount of data exacerbates the sparseness of the data, which leads to the poor accuracy of the results and affects the recommendation accuracy. Aiming at this problem,this study proposes a recommendation algorithm based on the combined similarity of users. The calculation of combined similarity of users is divided into two parts： the similarity of the user＇s preference for item attributes and the similarity of the demographic information between the users. The algorithm introduces the LDA model to calculate the preference for the user＇s item attribute, and the scoring data is only used as the screening basis when calculating so as to avoid using it directly as well as slow down the influence of sparse data on similarity calculation results. While the similarity between demographic information is measured by Hamming distance after the numerlization of demographic information.Experimental results show that the proposed algorithm is superior to the traditional collaborative filtering recommendation algorithm in recommendation accuracy.

关 键 词：协同过滤 稀疏数据 LDA 联合相似度 海明距离 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]