期刊文献+

面向用户信息需求的移动商务在线评论效用评价研究 被引量:7

Research on Reviews Utility Evaluation of Mobile Commerce Facing the Users' Information Needs
原文传递
导出
摘要 【目的/意义】面向用户信息需求为移动商务平台设计个性化的在线评论效用评价方法,满足用户对商品在线评论的个性化需求,辅助用户进行消费决策。【方法/过程】首先分析利用GA-BP神经网络设计个性化移动商务用户在线评论效用评价方法的可行性和意义,然后结合移动商务用户在线评论的特点,从评论内容、评论者、评论阅读者、评论时效性4个维度,选取10个影响评论效用的指标,基于GA-BP神经网络设计了效用评价方法。最后采集美团APP美食版块数据,通过与标准BP神经网络实验结果对比分析验证该方法的有效性和实用性。【结果/结论】基于GA-BP神经网络的移动商务用户在线评论效用评价方法具有较好的可行性和实用性,运用GA-BP神经网络稳定性高,能够提升效用评价的精度和效率。 [Purpose/significance] A personalized evaluation model of mobile user reviews is built to meet the needs of users, which reduces the cost and time of user information search, enhances the shopping experience of users, and helps users to make consumer decisions. [ Method/process ] Firstly, we analyze the feasibility and significance of the construction of mobile commerce user reviews utility evaluation model by GA-BP neural network.And then combined with the characteristics of mobile commerce users online reviews, comments, and comments from reviewers, readers comment timeliness 4 dimensions, we select 10 utility evaluation indexes and design the utility evaluation model by GA-BP neural network .Finally, we collect the data of APP food section, and use standard BP neural analysis to verify the validity and practicability of the model. [ Results/conclusion ] Evaluation of mobile commerce user utility GA-BP neural network model to accelerate the convergence speed of the network based on the improved utility evaluation accuracy, can accurately evaluate the effectiveness of online reviews, has better effectiveness and practicality.
出处 《情报科学》 CSSCI 北大核心 2018年第2期132-138,158,共8页 Information Science
基金 吉林大学研究生创新基金资助项目(2017082)
关键词 移动商务 在线评论 GA-BP神经网络 用户信息需求 效用评价 mobile commerce online reviews GA-BP neural network user information needs utility evaluation
  • 相关文献

参考文献12

二级参考文献147

  • 1闫强,孟跃.在线评论的感知有用性影响因素——基于在线影评的实证研究[J].中国管理科学,2013,21(S1):126-131. 被引量:67
  • 2杨芙清.软件工程技术发展思索[J].软件学报,2005,16(1):1-7. 被引量:267
  • 3郭国庆,杨学成,张杨.口碑传播对消费者态度的影响:一个理论模型[J].管理评论,2007,19(3):20-26. 被引量:100
  • 4韩家炜 Michelin K.数据挖掘:概念与技术[M].北京:机械工业出版社,2001..
  • 5Chevalier J A, Mayzlin D. The Effect of Word of Mouth on Sales : Online Book Reviews [ J . Journal of Marketing Research,2006,43 (3) :345 -354.
  • 6Ye Q, Zhang Z Q, Law R. Sentiment Classification of Online Re- views to Travel Destinations by Supervised Machine Learning Ap- proaches[ J ]. Expert Systems with Applications, 2009, 36 ( 3 ) : 6527 - 6535.
  • 7Miao Q L, Li Q D, Dai R W. AMAZING : A Sentiment Mining and Retrieval System [ J ]. Expert Systems with Applications, 2009, 36 (3) : 7192 -7198.
  • 8Liu J J,Cao Y B,Lin C Y,et al. Low -quality Product Review De- tection in Opinion Summarization[ C ]. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague: Associa- tion Computational Linguistics,2007:334 -342.
  • 9Zhang Z. Weighing Stars: Aggregating Online Product Reviews for Intelligent E - commerce Applications [ J ]. IEEE Intelligent Sys- tems, 2008, 23(5):42-49.
  • 10Lau R Y K, Liao S S Y, Xu K Q. An Empirical Study of Online Consumer Review Spam : A Design Science Approaeh [ C ]. In : Proceedings of the 31st International Conference on Information Sys- tems,St. Louis,USA. Aceociation of Information Systems,2010.

共引文献291

同被引文献103

引证文献7

二级引证文献44

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部