海量用户评论在APP更新设计中的参与作用挖掘

APP市场上用户发表的评论包含着对APP软件研发团队的有用信息,尤其是软件功能请求以及具有特定应用的用户体验内容,可以应用到APP软件的版本更新设计中.为了研究海量用户评论在APP软件更新和重新设计中的参与作用,本文提出一种基于词向量表征的句向量相似性计算模型,它可以用于度量来自更新日志文本和用户评论文本中的句子的相似性.然后,本文提出了APP产品的“更新日志-用户评论”匹配算法将不同语义匹配结果划分到不同的数据集.基于真实的APP市场上采集的海量APP软件升级日志数据和用户评论数据的实验证明了本文提出方法的有效性.研究结果还发现:APP开发团队对于用户评论内容的采纳不到20%,而且采纳的内容主要集中在APP软件功能方面.用户评论中众多内容指向企业的营销活动问题,这部分内容由于研发团队在企业运营中的管理角色所限,很少能够顾及.

Mining the participatory role of massive user reviews in the update design of APP software

The user reviews published on APP market contain useful information for the APP R&D team.In order to study the influence and mode of user reviews on APP software update design,we propose a sentence vector similarity calculation model based on word vector representation,which can be used to measure the similarity of sentences from update log text and user comment text.Then,we propose a"log-comment"matching algorithm to divide the different semantic matching result into different data sets.By collecting a large amount of APP software update logs and user reviews from an open APP market,our method found that the APP development team adopted less than 20%of the user reviews,and the content adopted was mainly focused on the APP software function.Many of the user reviews pointed to the marketing activities,however,these reviews can rarely be considered and corrected in the new version of an APP.It was partly due to the limited role of R&D team in company's daily operation.