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.

EGEScold startgraph embedding

0 likes · 9 min read

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

DataFunSummit

Aug 21, 2021 · Artificial Intelligence

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

This article reviews algorithmic solutions for cold‑start recommendation, covering the efficient use of side information, knowledge graphs, cross‑domain transfer, multi‑behavior signals, limited interaction data, explore‑exploit tactics, and additional practical scenarios, while summarizing key methods such as DropoutNet, MetaEmbedding, MWUF, MeLU and MetaHIN.

cold-startcross-domainknowledge graph

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Cold‑Start Recommendation: Algorithmic Approaches and Strategies

Alibaba Cloud Developer

Aug 29, 2018 · Artificial Intelligence

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

An in‑depth look at Alibaba’s billion‑scale graph embedding framework—GES and EGES—reveals how side‑information‑enhanced embeddings address user long‑tail coverage and cold‑start challenges, improving recommendation diversity and discovery across massive e‑commerce datasets and enabling real‑time personalized ranking.

Recommendation Systemscold starte‑commerce

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How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

Ctrip Technology

Jan 13, 2017 · Artificial Intelligence

Deep Learning Applications in Recommender Systems: A Hybrid Collaborative Filtering Model

This article reviews the early research on applying deep learning techniques such as autoencoders, stacked denoising autoencoders, and hybrid collaborative‑filtering models to recommender systems, describing the underlying matrix‑factorization theory, side‑information integration, experimental results, and future prospects.

AutoencoderHybrid Modelcollaborative filtering

0 likes · 13 min read

Deep Learning Applications in Recommender Systems: A Hybrid Collaborative Filtering Model