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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.
This article introduces graph modeling concepts, explains how they are applied to large‑scale information‑feed recall, details specific algorithms such as DeepWalk, LINE and GraphSAGE, describes feature engineering, loss design, training, deployment, evaluation, and discusses current challenges and future directions.
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.
The paper presents an optimized CTR prediction model for Tencent’s App Store that merges multi‑behavior shared embeddings, long‑term DeepWalk graph embeddings, BERT‑derived app description vectors, and attention‑based fusion, reducing parameters while improving bias, AUC, and recommendation performance for sparse, long‑tail data.
This article explains how 58.com applies various embedding methods—including ALS, Skip‑gram, and DeepWalk—to vectorize users and properties, improve similarity calculations, and enhance both recall and ranking stages of its real‑estate recommendation system, with detailed technical descriptions and evaluation results.
This article provides a comprehensive overview of graph embedding methods—including DeepWalk, LINE, node2vec, and EGES—explaining their algorithms, random‑walk strategies, proximity definitions, incorporation of side information, and their applications in large‑scale recommendation systems.