End-to-End User and Item Embeddings: Specializing Retrieval Representations for Ranking

Abstract

Industrial recommender systems typically operate in two stages: retrieving a candidate set from a large catalog, then ranking those candidates using contextual information. The ranking stage relies on features that summarize a user’s prior interactions with the system. These features are often carefully hand-crafted, and designing and maintaining them is time-consuming and computationally expensive. In this paper we propose leveraging the user and item embeddings already produced in the retrieval stage as input features to the downstream ranking model, and fine-tuning them for the ranking objective. We motivate this direction with A/B test results from a large-scale music streaming platform, where retrieval-stage user embeddings improve ranking performance even without fine-tuning. Our results suggest that representations learned for retrieval can reduce the reliance on expensive hand-crafted ranking features, and that fine-tuning them offers a promising path to further gains.