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首页> 外文期刊>International Journal of Production Research >Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach
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Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

机译：优化在线评论使用基于自学习粒子群的贝叶斯学习方法的产品属性分类启发

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摘要

Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritise customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A Bayesian Network Structure Learning model with the K2score maximisation objective is formulated and solved. A self-tailored metaheuristic approach that combines self-learning particle swarm optimisation (SLPSO) with the K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of 9 fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with the particle swarm optimisation/K2score, Genetic Algorithm/K2 score and ant colony optimisation/K2 score. Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure-based reviews are major components of information in fashion blogs that influence the customers.
机译：向新兴消费者的蓬勃发展的需求鞠躬，利用社交媒体内容，用于推断买方的信息，从事服务科学，数据分析，机器学习和相关领域的研究人员和从业者的越来越关注。目前的纸张旨在识别产品属性之间的结构关系，并随后在利用德国时尚博客的文本社交媒体数据来利用来自时尚博客的文本社交媒体数据，优先考虑客户偏好。制定并解决了具有K2Score最大目标的贝叶斯网络结构学习模型。将自动学习粒子群优化（SLPSO）与K2算法（SLPSOK2）结合的自身定制的成分型方法被用于破译最高刻度的结构。所提出的方法是在由9个时尚属性和18个问题集中组成的小型和大尺寸实例上实现的。通过SLPSOK2获得的结果与粒子群优化/ K2Score，遗传算法/ K2分数和蚁群优化/ K2分数进行比较。结果验证SLPSOK2在计算时间和解决方案质量方面优于测试用例的混合对应物。此外，该研究表明，心理满意度，历史复兴，季节性信息和事实和图形审查是影响客户的时尚博客信息的主要组成部分。

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