Evergreen missions: modeling long term user interests in e-commerce

Identifying long term user interests, i.e., “evergreen missions”, in retail and e-commerce is a challenging yet important problem. In this work, we propose a machine learning system that is able to identify a user’s long term arbitrary interests by leveraging their site interaction history. Our contribution is a system that is composed of three components (1) projecting our listing inventory to an embedding space with a combination of supervised/unsupervised modeling, (2) inferring personalized interests from the embedding space to a user base with attributed interactions, and (3) estimating the repeat interaction rate with inventory through a rigorous statistical approach. Additionally, we provide novel insights by leveraging the supervised neural network model to produce a clustering approach for interest discovery. The approach has been implemented, validated, and rigorously A/B experimented with and is currently in production at Etsy, Inc., powering its several modules.