Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

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...

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Bibliographic Details
Published in:International journal of production research 2019-05, Vol.57 (10), p.3099-3120
Main Authors: Maiyar, Lohithaksha M., Cho, SangJe, Tiwari, Manoj Kumar, Thoben, Klaus-Dieter, Kiritsis, Dimitris
Format: Article
Language:English
Subjects:
Analytics
Ant colony optimization
Bayesian analysis
Bayesian network structure learning
Bowing
Computing time
customer preference ordering
Customer satisfaction
Customers
Digital media
Domains
fashion products
Genetic algorithms
Heuristic methods
Machine learning
self-learning particle swarm optimisation
Social networks
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Summary: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1535724