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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：使用基于自学习粒子群的贝叶斯学习方法优化在线评论启发的产品属性分类

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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：为了满足在线消费者的迅速增长的需求，利用社会媒体内容来推断以购买者为中心的信息正越来越受到来自服务科学，数据分析，机器学习和相关领域的研究人员和从业人员的关注。本白皮书旨在确定产品属性之间的结构关系，并随后针对这些属性确定客户偏好的优先级，同时利用源自德国时尚博客的文本社交媒体数据。建立并求解了具有K2score最大化目标的贝叶斯网络结构学习模型。一种结合了自学习粒子群优化（SLPSO）和K2算法（SLPSOK2）的自量身启发式方法，用于解密得分最高的结构。所提出的方法是在由9个时尚属性和18个问题集组成的小型，中型和大型实例上实现的。将SLPSOK2获得的结果与粒子群优化/ K2分数，遗传算法/ K2分数和蚁群优化/ K2分数进行比较。结果证明，在计算时间和解决方案质量方面，SLPSOK2的性能优于其混合同类产品。此外，研究表明，心理满意度，历史复兴，季节性信息和事实以及基于人物的评论是影响消费者的时尚博客信息的主要组成部分。

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