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DataFunSummit
DataFunSummit
Mar 6, 2022 · Artificial Intelligence

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

This article traces the development of embedding methods—from the early word2vec model through item2vec, DeepWalk, Node2vec, EGES, HERec, GraphRT, and target‑fitting approaches like DSSM and YouTube recommendation—highlighting how sequence‑construction and target‑fitting paradigms have shaped modern recommendation systems and AI applications.

Graph Neural NetworksRecommendation systemsdeep learning
0 likes · 26 min read
The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks
Sohu Tech Products
Sohu Tech Products
May 27, 2020 · Artificial Intelligence

Overview of Embedding Methods: From Word2Vec to Item2Vec and Dual‑Tower Models in Recommendation Systems

This article provides a comprehensive overview of embedding techniques, explaining their role in deep learning recommendation systems, detailing Word2Vec and its Skip‑gram model with negative sampling and hierarchical softmax, and extending the discussion to Item2Vec and dual‑tower architectures for item representation.

Recommendation systemsWord2Vecdeep learning
0 likes · 15 min read
Overview of Embedding Methods: From Word2Vec to Item2Vec and Dual‑Tower Models in Recommendation Systems
Meitu Technology
Meitu Technology
Jul 17, 2018 · Artificial Intelligence

Video Clustering Techniques for Personalized Recommendation in Meipai

Meipai’s personalized recommendation system leverages massive user‑behavior data to build behavior‑driven video clusters—evolving from TopicModel through Item2vec and Keyword Propagation to a DSSM deep model—boosting ranking AUC, enhancing UI diversity, similar‑video search, niche discovery, and feature engineering.

DSSMTopic Modelingitem2vec
0 likes · 22 min read
Video Clustering Techniques for Personalized Recommendation in Meipai