Abstract

The accurate categorization of offers is a prominent challenge in the ecommerce industry. Effective offer categorization is critical to ensuring that products are displayed to the right customers and can provide valuable insights into customer behavior and preferences. However, categorizing offers at scale can be especially challenging when dealing with large product catalogs. To address this, it is important to use a robust system that can accurately categorize offers based on factors such as product attributes, price, and to leverage automation tools such as machine learning algorithms where possible.

Offer categorization can also help with search relevance, sorting offers to products, and facilitating easier indexing by search engines such as Google. Mapping offers to Google's taxonomy, which comprises of nearly 6000 hierarchical classes, can quickly become challenging when working at scale. To ensure consistent categorization across different systems and platforms, it is important to establish clear taxonomy standards and to continually review and adjust the categorization system to ensure that it remains accurate and effective.

In this talk, we will discuss the challenges encountered in categorizing offers, the strategies we employed to address scaling and categorizing concerns, as well as the issues and limitations we overcame to achieve success.

Zeeshan Dar

Senior Machine Learning Engineer @ Prisjakt

Zeeshan discovered his passion for machine learning during his Bachelor's studies. After founding his own company, he gained further knowledge and experience by working as a Data Scientist at a consultant company for three years. He pursued a Master's degree at Chalmers University in Sweden, with a focus on machine learning, and subsequently worked as a data science consultant for a prominent autonomous vehicle company for nearly three years. Currently, he works as a senior machine learning engineer at Prisjakt, where he brings extensive expertise to develop innovative and effective products.