OPPO Advertising Recall Algorithm: Architecture, Model Selection, Offline Evaluation, Sample Optimization, and Future Directions

This article presents OPPO's comprehensive advertising recall system, detailing the transition from the old to the new architecture with ANN support, the selection of main‑road recall models, the construction of offline evaluation metrics, sample optimization techniques, model enhancements, multi‑scenario training strategies, and outlook for future improvements.

Advertisingdual-tower modellarge-scale classification

0 likes · 24 min read

Architect

Jan 12, 2024 · Artificial Intelligence

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.

Embedding Retrievaldivide and conquerindustry insights

0 likes · 7 min read

Can Divide‑and‑Conquer Boost Embedding‑Based Retrieval in Recommenders?

DataFunSummit

Oct 8, 2023 · Artificial Intelligence

NLP Techniques for Financial Risk Control: Text Modeling, Non‑Text Modeling, Long‑Text Handling, Multi‑Modal Fusion and Sample Optimization

This article presents a comprehensive overview of how natural language processing is applied to financial risk control, covering text and non‑text sequence modeling, tokenization strategies, transformer‑based long‑text architectures, multi‑modal fusion methods, pre‑training techniques and practical sample‑optimization approaches.

NLPText Modelingai

0 likes · 22 min read

NLP Techniques for Financial Risk Control: Text Modeling, Non‑Text Modeling, Long‑Text Handling, Multi‑Modal Fusion and Sample Optimization