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出 处:《情报科学》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)
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.
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