Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship

作     者：Shengxiang Gao Zhengtao Yu Linbin Shi Xin Yan Haixia Song 

作者机构：School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2015年第2卷第4期

页      面：403-411页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China(611750 68,61472168,61163004) Natural Science Foundation of Yunnan Province(2013FA130) Talent Promotion Project of Ministry of Science and Technology(2014HE001) 

主　　题：Algorithms Collaborative filtering Factorization Rating Statistics 

摘      要：The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert s rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the cold start problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation (LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert, and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts. © 2014 Chinese Association of Automation.