北京交通大学学报2017，Vol.41Issue(2)：1-7,7.DOI:10.11860/j.issn.1673-0291.2017.02.001
基于关系图邻接矩阵逼近的推荐系统
Graph adjacency matrix approximation based recommendation system
摘要
Abstract
In graph based recommendation methods,the goal of the graph constraint is to preserve the consistency of user relationships (item relationships) between high dimensional user representation space (item representation space) and low dimensional latent user representation space.Instead of applying the traditional Laplacian matrix based consistency constraint,a graph adjacency matrix approximation based recommendation model is proposed.In essence,the matrix approximation plays a role of directly imposing a consistency constraint on the different similarity metric spaces.Thus,not only the consistency of the user relationships (the item relationships) from different representation spaces can be well preserved,but also,the local over-fitting problem can be avoided to some extent.Experimental results on EachMovie and MovieLens datasets show the effectiveness of the proposed method.关键词
推荐系统/协同过滤/因子分解/图模型/梯度下降法Key words
recommender system/collaborative filtering/matrix factorization/graph model/gradient descent method分类
信息技术与安全科学引用本文复制引用
朱振峰,高红格,赵耀..基于关系图邻接矩阵逼近的推荐系统[J].北京交通大学学报,2017,41(2):1-7,7.基金项目
国家自然科学基金(61572068,61532005) （61572068,61532005）
教育部新世纪优秀人才支持计划项目(NCET-13-0661) （NCET-13-0661）
中央高校基本科研业务费专项资金(2015JBM039)National Natural Science Foundation of China(61572068,61532005) （2015JBM039）
Program for the New Century Excellent Talents in Universities of China(NCET-13-0661) （NCET-13-0661）
Fundamental Research Funds for the Central Universities(2015JBM039) （2015JBM039）
