EGES | BestHub

Tongcheng Travel Technology Center

May 9, 2023 · Artificial Intelligence

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

This article presents EGES, a graph‑embedding model that incorporates side information to construct a directed user graph, apply biased random‑walk sampling, and train weighted Skip‑Gram embeddings, thereby improving large‑scale user acquisition and addressing cold‑start challenges in recommendation systems.

Cold StartEGESgraph embedding

0 likes · 9 min read

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

Zhuanzhuan Tech

Sep 14, 2022 · Artificial Intelligence

Graph Embedding Algorithms and Their Application in Zhuanzhuan Recommendation System

This article introduces the fundamentals of recommendation systems, explains Zhuanzhuan's main recommendation scenarios and pipeline, and details three graph embedding methods—DeepWalk, node2vec, and EGES—along with their practical implementations in recall and coarse‑ranking stages.

DeepWalkEGESe‑commerce

0 likes · 17 min read

Graph Embedding Algorithms and Their Application in Zhuanzhuan Recommendation System

58 Tech

Dec 16, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58's commercial recruitment recommendation system, covering the business scenario, system architecture, regional and behavior‑based recall methods, various ranking models—including coarse‑ranking, dual‑tower, DIN‑bias, and multitask W3DA—and future optimization directions.

DBSCANEGESRanking

0 likes · 20 min read

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

DataFunSummit

Dec 12, 2021 · Artificial Intelligence

Design and Implementation of 58.com Commercial Recruitment Recommendation System

This article presents a comprehensive overview of the 58.com commercial recruitment recommendation system, detailing its business challenges, system architecture, region‑based and behavior‑based recall strategies, coarse‑ and fine‑ranking models, bias handling, evaluation methods, and future directions.

DBSCANEGESRanking

0 likes · 20 min read

Design and Implementation of 58.com Commercial Recruitment Recommendation System

Tencent Cloud Developer

Jun 9, 2021 · Artificial Intelligence

Overview of Common Graph Embedding Methods in Industry

The article surveys six widely‑used graph‑embedding techniques—DeepWalk, Node2Vec, LINE, SDNE, EGES and Metapath2Vec—explaining how each transforms graph topology into low‑dimensional vectors via random walks, biased sampling, proximity‑based objectives, deep auto‑encoders, side‑information integration, or meta‑path‑guided walks for industrial applications.

DeepWalkEGESMetapath2Vec

0 likes · 14 min read

Overview of Common Graph Embedding Methods in Industry