PAW Business TRACK 3: CASE STUDIES - Cross-industry business applications of machine learning
Improving search relevance
Case Study: Instacart
Global grocery industry is worth a massive $5.7T of which only 10% is currently online. As online grocery business accelerated from 2020, Instacart search, which supports one of the largest catalog of grocery items in the world, started facing new challenges. This talk focuses on these unique challenges and how we improved the performance of our machine learning models to significantly improve search relevance and our business metrics as a result.
Topics of discussion will include:
* Using transformer-based NLP models for understanding a user's intent in retailers from non-grocery verticals who are new to our platform.
* Using ML models that leverage a Knowledge graph to improve the diversity of the recalled results and improve basket size.
* Limitations we faced from keyword based search, and deep dive into Embedding based Retrieval techniques to capture latent user intent.
* Building a multi-objective Autocomplete ranking model for helping users explore the full breadth of content in Instacart.
* ML models to control the quality of search results to avoid showing irrelevant and embarrassing products especially for tail queries or retailers that suffer from the cold-start problem.
Senior Manager, Algorithms and Machine Learning