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Title |
AraProdMatch: A Machine Learning Approach for Product Matching in E-Commerce |
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Author |
Aisha Alabdullatif and Monira Aloud |
| Citation |
Vol. 21 No. 4 pp. 214-222 |
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Abstract |
Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure. |
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Keywords |
product matching; artificial neural network; consumer decision-making; deep learning; e-commerce. |
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