融合Motif结构高阶相似度的文献推荐算法  

Literature Recommendation Algorithm Integrating High-Order Similarity of Motif Structure

作　　者：陈柳 郭宇红[1] Chen Liu;Guo Yuhong(School of Cyber Science and Engineering,University of International Relations,Beijing 100091,China)

机构地区：[1]国际关系学院网络空间安全学院,北京100091

出　　处：《数据分析与知识发现》2023年第7期146-155,共10页Data Analysis and Knowledge Discovery

基　　金：国际关系学院大学生学术支持计划项目(项目编号:3262021SYJ007)的研究成果之一。

摘　　要：【目的】将协同过滤方法应用到文献推荐领域,融入用户余弦相似度网络中Motif结构反映出的高阶相似特征,提高推荐的质量。【方法】通过用户收藏文献的行为信息和文献间的引用关系构建用户对文献的偏好数据;在基于用户-文献收藏行为信息的用户余弦相似度网络中,利用网络中的子图——Motif结构捕获高阶相似度;最后将用户余弦相似度和基于Motif结构的高阶相似度融入矩阵分解推荐算法中,预测用户对文献的偏好。【结果】相较于传统的矩阵分解推荐算法,本文算法在RMSE和MAE指标上分别降低0.0482和0.0379。【局限】未考虑文献的时间衰减性。【结论】本文算法降低了用户偏好预测误差,提高了推荐质量。[Objective]This paper applies the collaborative filtering method to the field of literature recommendation.It incorporates high-order similarity features reflected by the Motif structure in the user cosine similarity network to improve the recommendation quality.[Methods]Firstly,we constructed the user preference data for literature using their behavior information of collecting literature and the citation relationship between literature.Secondly,in the user cosine similarity network based on user literature collection behavior information,we captured the high-order similarity with subgraph—Motif structure within the network.Finally,we integrated user cosine and high-order similarity based on Motif structure into the matrix factorization recommendation algorithm to predict user preferences for literature.[Results]Compared with the traditional matrix factorization recommendation algorithms,this algorithm’s RMSE and MAE metrics were reduced by 0.0482 and 0.0379,respectively.[Limitations]The proposed algorithm does not consider the temporal decay of the literature.[Conclusions]The new algorithm reduces the prediction error of user preferences and improves the literature recommendation quality.

关 键 词：文献推荐 MOTIF 结构 用户高阶相似度 矩阵分解 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] G250[自动化与计算机技术—计算机科学与技术]