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Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 24, 2024 · Artificial Intelligence

Pre‑Ranking in Recommendation Systems: Model and Sample Optimization Practices at Zhuanzhuan Home Page

This article reviews the role of pre‑ranking in multi‑stage recommendation pipelines, compares dual‑tower and fully‑connected DNN models, discusses negative and positive sample selection strategies, and presents Zhuanzhuan's practical improvements in model architecture and traffic‑pool allocation to boost precision and diversity.

Model Optimizationdual-towerpre‑ranking
0 likes · 16 min read
Pre‑Ranking in Recommendation Systems: Model and Sample Optimization Practices at Zhuanzhuan Home Page
NewBeeNLP
NewBeeNLP
Feb 12, 2024 · Artificial Intelligence

Beyond Dual‑Tower: Advanced Distillation and Interaction Techniques for Recommendation Systems

This article reviews recent advances that enhance dual‑tower recommendation models by injecting interaction information through various knowledge‑distillation strategies and interaction‑enhanced architectures, summarizing methods such as PFD, ENDX, TRMD, VIRT, Distilled‑DualEncoder, ERNIE‑Search, ColBert, IntTower and MVKE.

AI researchdual-towerinteraction modeling
0 likes · 13 min read
Beyond Dual‑Tower: Advanced Distillation and Interaction Techniques for Recommendation Systems
DeWu Technology
DeWu Technology
Dec 20, 2023 · Artificial Intelligence

Coarse Ranking in Recommenders: Key Strategies, Metrics & Optimizations

This article systematically reviews the coarse‑ranking stage of recommendation systems, comparing it with recall and fine‑ranking, defining evaluation metrics, detailing sample design, presenting two technical routes, and exploring optimization directions such as dual‑tower models, knowledge distillation, lightweight fully‑connected layers, multi‑objective and multi‑scenario modeling, followed by practical case studies and results.

Evaluation Metricscoarse rankingdual-tower
0 likes · 22 min read
Coarse Ranking in Recommenders: Key Strategies, Metrics & Optimizations
Youzan Coder
Youzan Coder
Jul 11, 2022 · Artificial Intelligence

How Contrastive Learning Revolutionizes Product Term Prediction in E‑commerce

By leveraging contrastive learning and large‑scale click‑through data, the article details a dual‑tower model that encodes product titles and queries, explains loss functions, batch‑negative sampling, distributed training tricks, and demonstrates how this approach outperforms traditional NER for product term and category prediction.

Distributed TrainingE-commerce AIInfoNCE
0 likes · 16 min read
How Contrastive Learning Revolutionizes Product Term Prediction in E‑commerce
DeWu Technology
DeWu Technology
Feb 7, 2022 · Artificial Intelligence

Generalized Recommendation Solution for Transaction Scenarios

DeWu’s e‑commerce platform consolidated dozens of small‑scale transaction scenes into a universal personalized recommendation system by adopting a user‑to‑item DSSM dual‑tower model with unified sampling, category‑aware negative mining, cosine‑normalized embeddings, and real‑time serving, boosting click‑through rates by over 10% across all scenarios.

DSSMdual-towere‑commerce
0 likes · 13 min read
Generalized Recommendation Solution for Transaction Scenarios
58 Tech
58 Tech
Apr 9, 2021 · Artificial Intelligence

Vectorized Recall and Dual‑Tower Model for Home Page Recommendation at 58.com

This article details how 58.com improved its home‑page recommendation system by introducing vectorized recall with Word2Vec, optimizing negative sampling, deploying FAISS for fast nearest‑neighbor search, and later adopting a dual‑tower deep learning model with user interest features, achieving higher click‑through and conversion rates.

FAISSWord2Vecdual-tower
0 likes · 19 min read
Vectorized Recall and Dual‑Tower Model for Home Page Recommendation at 58.com