Sequence models for Contextual Recommendations at Instacart
Authors: Pradeep Karuturi, Young Rao, Sharath Rao, Shishir Kumar Prasad Key contributors: Brian Lin, Cheng Jia, Karuna Ahuja, Shrikar Archak, Jichao Zhang, Taesik Na, Haixun Wang
At Instacart, we help our users find their favorite products organically or discover new ones through Ads across various shopping surfaces such as search, browse and recommendations. Behind the scenes, we use machine learning algorithms to power these surfaces to provide a delightful user experience. Each of these surfaces have diverse optimization goals and multi-step ranking pipelines (retrieval, ranking, re-ranking). For example, while organic content is often optimized for user engagement and transaction revenue, sponsored content additionally takes into consideration advertiser value and ad revenue. In this blogpost, we describe how we built a centralized contextual retrieval system that powers diverse recommendation surfaces, even though their end goals and ranking layers are different. Having a common retrieval system across both ads and organic surfaces has lowered our maintenance costs and allowed us to deprecate many legacy ad hoc retrieval systems. Using in-session contextual signals, we built a BERT-like language model to power sequence recommendations for this system.
Our customers use Instacart for the convenience and time-savings we provide and often choose to fulfill the weekly shopping needs of their entire family by placing large basket orders. We take pride in making their shopping experience as efficient and effortless as possible. Our contextual retrieval system reacts in real time to a user’s actions within a shopping session and retrieves products relevant to that session. For example, when a user adds pancake mix to their cart, views bacon and later adds eggs to their cart, there is probably a breakfast intent and our contextual recommendation system will retrieve breakfast products as shown in the below diagram:
