Helping consumers to overcome information overload with a diversified online review subset  

作　　者：Jin Zhang Zhangwen Weng Naichen Ni 

机构地区：[1]School of Business,Renmin University of China,Beijing 100872,China [2]Department of Statistics,University of Michigan,Ann Arbor MI48109,USA

出　　处：《Frontiers of Business Research in China》2019年第3期319-343,共25页中国高等学校学术文摘·工商管理研究（英文版）

摘　　要：Redundant online reviews often have a negative impact on the efficiency of consumers' decisi on-making in their on line shopping.A feasible solution for business analytics is to select a review subset from the original review corpus for consumers,which is called review selection.This study aims to address the diversified review selection problem,and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection(SA-DRS)that considers the semantic relationship of review features and the con tent diversity of selected reviews simultaneously.SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation(LDA)topic model and the Word2vec model to measure the topic relation and word context relation.Based on the established feature taxonomy,the similarity between each pair of reviews is defined and the review quality is estimated as well.Fin ally,diversified,high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method,forming the selected review subset.Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches.

关 键 词：Business analytics ONLINE REVIEWS Feature taxonomy Diversified SUBSET REVIEW SELECTION Simulated annealing-diversified REVIEW selection(SA-DRS) ECOMMERCE 

分 类 号：O17[理学—数学]