Abstract
Product classification in E-commerce platforms aim to classify a product into correct category, in order to obtain a better guiding service for customer to purchase. Language model is usually used to encode product title into product embedding, and then fed into a classifier for multi-class classification. However, the product title is usually ambiguous and noisy, leading to poor prediction performance. To address this issue, we propose a novel pretrained E-commerce knowledge graph (PEKG) model to learn the representation of product from EKG, and then used it for fine-tuning. We formulate the pretraining on EKG as a multi-view learning problem, where the EKG is divided into four views. From PEKG, user and product representation is proposed to learn via aggregation of its neighbor information, and the semantic meaning from the EKG is learned via translation-based method. We experimentally prove that our proposal significantly outperforms baseline,showing that the PEKG can learn useful representation of product.
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Wong, CM., Vong, CM., Zhou, Y. (2023). Pretrained E-commerce Knowledge Graph Model for Product Classification. In: Björk, KM. (eds) Proceedings of ELM 2021. ELM 2021. Proceedings in Adaptation, Learning and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-031-21678-7_1
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DOI: https://doi.org/10.1007/978-3-031-21678-7_1
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