Can Divide‑and‑Conquer Boost Embedding‑Based Retrieval in Recommenders?
The article reviews the arXiv paper “Divide and Conquer: Towards Better Embedding‑based Retrieval for Recommender Systems from a Multi‑task Perspective”, explaining how grouping candidates, balancing easy and hard negatives, and using multi‑interest user vectors can improve recall performance in large‑scale recommendation pipelines.
