融合语义相似度的协同过滤推荐算法  

Collaborative filtering recommendation algorithm based on semantic similarity

作　　者：王根生[1,2] 潘方正 WANG Gensheng;PAN Fangzheng(Computer Practice Teaching Center, Jiangxi University of Finance and Economics,Nanchang 330013, China;School of Humanities, Jiangxi University of Finance and Economic, Nanchang 330013, China)

机构地区：[1]江西财经大学计算机实践教学中心,江西南昌330013 [2]江西财经大学人文学院,江西南昌330013

出　　处：《中国科学技术大学学报》2019年第10期835-841,共7页JUSTC

基　　金：国家自然科学基金资(71461012);江西省教育厅科技资助项目(GJJ181550);江西省高校人文社科项目(GL19110);深圳市哲学社会科学规划课题(SZ2019D050)资助。

摘　　要：针对协同过滤推荐算法没有考虑推荐对象间语义关系的问题,提出一种融合推荐对象语义相似度的改进型协同过滤推荐算法.首先利用知识图谱表示学习算法将推荐对象的语义信息嵌入到一个低维语义空间;然后计算推荐对象之间的语义相似度,把该语义相似度融合到协同过滤推荐算法的相似度计算中,弥补协同过滤推荐算法没有考虑推荐对象自身语义知识的缺陷.实验结果表明,该改进型算法相比传统协同过滤推荐算法,具有更高的准确率、召回率和覆盖率.To solve the problem that collaborative filtering recommendation algorithm does not consider the semantic relationship between recommendation objects,an improved collaborative filtering recommendation algorithm based on semantic similarity of recommendation objects is proposed.First,the semantic information of the recommended object is embedded into a low dimensional semantic space by using the knowledge map representation learning algorithm;then the semantic similarity between the recommended objects is calculated and integrated into the similarity calculation of collaborative filtering recommendation algorithm,thus compensating for the shortcoming that the collaborative filtering recommendation algorithm does not consider the semantic knowledge of the recommendation object.The experimental results show that the improved algorithm has higher accuracy,recall and coverage than the traditional collaborative filtering recommendation algorithm.

关 键 词：推荐算法 协同过滤 知识图谱 表示学习 语义相似度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]