Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach
Issue Date
2018-10-24Subjects
customer preference orderingmachine learning
Bayesian network structure learning
fashion products
self-learning particle swarm optimisation
Subject Categories::G760 Machine Learning
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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.Citation
Maiyar LM, Cho S, Tiwari MK, Thoben KD, Kiritsis D (2019) 'Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach', International Journal of Production Research, 57 (10), pp.3099-3120.Publisher
Taylor and Francis Ltd.Additional Links
https://www.tandfonline.com/doi/abs/10.1080/00207543.2018.1535724Type
ArticleLanguage
enISSN
0020-7543EISSN
1366-588XScopus Count
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