Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

The paper introduces a graph‑based weakly supervised contrastive learning framework that uses heterogeneous user‑behavior graphs, e‑commerce‑specific augmentations, and a hybrid fine‑tuning/transfer learning strategy to improve semantic relevance matching between queries and product titles, achieving significant gains on a large‑scale Taobao dataset.

Weak Supervisioncontrastive learninge‑commerce

0 likes · 12 min read

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

Meituan Technology Team

Jul 9, 2020 · Artificial Intelligence

Optimizing Meituan Search Ranking with BERT: Methods and Practices

The Meituan Search team boosted ranking relevance by training a domain‑specific BERT, applying data augmentation, brand‑sample optimization, knowledge‑graph fusion, multi‑task and pairwise fine‑tuning, joint end‑to‑end training with LambdaLoss ranking models, and compressing the model for low‑latency inference, delivering up to +925 BP offline accuracy gains and measurable CTR and NDCG improvements in production.

BERTknowledge distillationmachine learning

0 likes · 34 min read

Optimizing Meituan Search Ranking with BERT: Methods and Practices

DataFunTalk

May 21, 2020 · Artificial Intelligence

Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices

This article reviews the factors influencing search results, explains why query expansion is crucial for improving recall, surveys various sources of expansion terms, describes probabilistic and translation‑based models, and offers practical recommendations for building effective, data‑driven query expansion pipelines.

Knowledge Graphinformation retrievalmachine learning

0 likes · 11 min read

Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices

DataFunTalk

Jul 16, 2019 · Artificial Intelligence

Search Advertising and Ad Recall: Business Logic, Semantic Relevance, and Deep Learning Models at 360

This article explains the architecture of 360's search advertising system, detailing its ad recall, ranking, and display modules, illustrates exact‑match and semantic recall methods with a case study, and reviews the evolution from feature‑engineered GBDT models to deep learning approaches such as DSSM, ESIM, and BERT, including data preparation, training, and performance evaluation.

BERTDSSMad recall

0 likes · 10 min read