Abstract
Product categorization is a necessary feature of e-commerce websites since it ensures that the websites retrieve related items from the product taxonomy tree accurately. In traditional product categorization methods, machine learning and deep learning classification algorithms are frequently applied. These cater towards product categorization by taking input and then categorising it in one of the predefined categories. In this paper, we propose a machine translation-based solution for e-commerce product categorization. We convert the natural language description of a product into a token sequence that reflects the root leaf taxonomy of the product category. In the experiment, three e-commerce product datasets (Flipkart, Walmart, Amazon) have been combined to substantiate the applicability of the natural language models implemented. We demonstrate that ensembling sequence-to-sequence neural networks and the transformer model outperforms state-of-the-art product categorization algorithms in terms of predicted accuracy. In addition, the accuracy comparison for machine learning classification (KNN, Random Forests, SVM), deep learning classification (LSTM, BERT) and neural machine translation models (Seq2Seq, Seq2Seq + Transformer) is shown to validate ensembling the two elements as a better method. In conclusion, we illustrate that attentional sequential models generate product category labels without supervised constraints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yundi Li M, Tan L, Kok S (2018) Don’t classify, translate: multi-level e-commerce product categorization via machine translation. Workshop on information technologies and systems 2018 (WITS2018) arXiv:1812.05774v1 [cs.CL]
Yundi Li M, Tan L, Kok S, Szymanska E (2018) Unconstrained production categorization with sequence-to-sequence models. Rakuten data challenge at the 2018 SIGIR workshop on ecommerce
Kalchbrenner N, Blunsom P (2013) Recurrent continuous translation models. In Proceedings of 2013 conference on empirical methods in natural language processing, 1700–1709
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In Proceedings of the 27th international conference on neural information processing systems, 3104–3112
Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In Proceedings of the third international conference on learning representations
Hassan H, Aue A, Chen C, Chowdhary V, Clark J, Federmann C, Huang X, JunczysDowmunt M, Lewis W, Li M, Liu S, Liu TY, Luo R, Menezes A, Qin T, Seide F, Tan X, Tian F, Wu L, Wu S, Xia Y, Zhang D, Zhang Z, Zhou M (2018) Achieving human parity on automatic chinese to english news translation. Comput Res Repository, abs/1803.05567
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org
Sun C, Rampalli N, Yang F, Doan A (2014) Chimera: Large-scale classification using machine learning, rules, and crowdsourcing. Proc VLDB Endowment 7:1529–1540
Pane RA, Mubarok MS, Huda NS, Adiwijaya (2018) “A multi-label classification on topics of quranic verses in english translation using multinomial Naive Bayes,” 2018 6th international conference on information and communication technology (ICoICT) pp 481–484, https://doi.org/10.1109/ICoICT.2018.8528777
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. arXiv:1409.3215v3 [cs.CL]
Yang P, Ma S, Zhang Y, Lin J, Su Q, Sun X (2018) A deep reinforced sequence-to-set model for multi-label text classification. arXiv:1809.03118v1 [cs.CL]
McCann B, Bradbury J, Xiong C, Socher R (2017) Learned in translation: contextualized word vectors. v1
Xu R, Yang Y, Liu H, Hsi A (2016) Cross-lingual text classification via model translation with limited dictionaries
Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on empirical methods in natural language processing, 1412–1421
Wu F, Fan A, Baevski A, Dauphin YN, Auli M (2019) Pay less attention with lightweight and dynamic convolutions. arXiv:1901.10430v2 [cs.CL]
Lin J, Su Q, Yang P, Ma S, Sun X (2018) Semantic-unit-based dilated convolution for multi-label text classification. arXiv:1808.08561v2 [cs.CL]
Altman NS (1992) “An introduction to kernel and nearest-neighbor nonparametric regression”. Am Statis 46(3):175–185
Cortes C, Vapnik VN (1995) “Support-vector networks”. Machine Learning. 20(3):273–297. CiteSeerX 10.1.1.15.9362. https://doi.org/10.1007/BF00994018. S2CID 206787478
Ho TK (1995) Random decision forests. Proceedings of the 3rd international conference on document analysis and recognition, Montreal, QC, 14–16 August 1995. pp 278–282
Van Houdt G, Mosquera C, Nápoles G (2020) A review on the long short-term memory model. Artif Intell Rev 53:5929–5955. https://doi.org/10.1007/s10462-020-09838-1l
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser LU, Polosukhin I (2017) Attention is all you need. In Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30, Curran Associates, Inc., 5998–6008
Howard J, Ruder S (2018) Universal Language Model Fine-tuning for Text Classification. arXiv:1801.06146v5 [cs.CL]
Yu H-F, Ho C-H, Arunachalam P, Somaiya M, Lin C-J (2013) Product title classification versus text classification. Technical report, Department of Computer Science, National Taiwan University, Taipei, Taiwan
Xia Y, Levine A, Das P, Di Fabbrizio G, Shinzato K, Datta A (2017) Large-scale categorization of japanese product titles using neural attention models. In Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Vol 2, Short Papers. Association for Computational Linguistics
Shen D, Ruvini JD, Sarwar B (2012) Large-scale item categorization for e-commerce. In Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12, New York, NY, USA. ACM, 595–604
Shafto P, Coley JD (2003) Development of categorization and reasoning in the natural world: novices to experts, naive similarity to ecological knowledge. J Exp Psychol Learn Mem Cogn 29(4):641–649
Ross BH, Murphy GL (1999) Food for thought: cross-classification and category organization in a complex real-world domain. Cogn Psychol 38(4):495–553
McAuley JJ, Pandey R, Leskovec J (2015) Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 785–794. ACM
Kozareva Z (2015) Everyone likes shopping! multi-class product categorization for e-commerce. In NAACL HLT 2015, The 2015 conference of the North American Chapter of the association for computational linguistics, Human Language Technologies
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, P., Raman, S. (2022). Translate2Classify: Machine Translation for E-Commerce Product Categorization in Comparison with Machine Learning & Deep Learning Classification. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 91. Springer, Singapore. https://doi.org/10.1007/978-981-16-6285-0_60
Download citation
DOI: https://doi.org/10.1007/978-981-16-6285-0_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6284-3
Online ISBN: 978-981-16-6285-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)