5 Papers on Product Classification Every Data Scientist Should Read

5 Papers on Product Classification Every Data Scientist Should Read

5 Papers on Product Classification Every Data Scientist Should Read. The following papers on product categorization represent essential reading in the field and offer novel approaches to product classification tasks.

Product categorization/product classification is the organization of products into their respective departments or categories. A large part of the process is the design of the product taxonomy as a whole. 

Product categorization was initially a text classification task that analyzed the product’s title to choose the appropriate category. However, numerous methods have been developed which take into account the product title, description, images, and other available metadata. 

The following papers on product categorization represent essential reading in the field and offer novel approaches to  product classification  tasks.

1. Don’t Classify, Translate

In this paper, researchers from the National University of Singapore and the Rakuten Institute of Technology propose and explain a novel machine translation approach to product categorization. The experiment uses the Rakuten Data Challenge and Rakuten Ichiba datasets. 

Their method translates or converts a product’s description into a sequence of tokens which represent a root-to-leaf path to the correct category. Using this method, they are also able to propose meaningful new paths in the taxonomy.

The researchers state that their method outperforms many of the existing classification algorithms commonly used in machine learning today.

  • Published/Last Updated – Dec. 14, 2018
  • Authors and Contributors – Maggie Yundi Li (National University of Singapore), Stanley Kok (National University of Singapore), and Liling Tan (Rakuten Institute of Technology)

machine learning ecommerce machine learning &amp ai product category

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