skip-gram | BestHub

Ximalaya Technology Team

Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

Rankingadvertisinggraph neural network

0 likes · 18 min read

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

DataFunTalk

Oct 18, 2020 · Artificial Intelligence

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

This article reviews how skip‑gram‑based graph embedding methods such as DeepWalk, LINE and node2vec can be interpreted as matrix factorization, explains the NetMF and NetSMF frameworks that use sparse matrix approximations and random SVD for large‑scale networks, and discusses extensions like GATNE and deep clustering approaches to address practical challenges in constructing and applying graph representations.

Graph Neural Networksgraph embeddingmatrix factorization

0 likes · 13 min read

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

DataFunTalk

Jun 10, 2020 · Artificial Intelligence

Embedding Techniques for Real Estate Recommendation at 58.com

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.

ALSDeepWalkReal Estate

0 likes · 16 min read

Embedding Techniques for Real Estate Recommendation at 58.com

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

DataFunTalk

Jun 25, 2019 · Artificial Intelligence

Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms

This article describes how Tujia applied embedding techniques, particularly a Skip‑Gram model, to build an item‑to‑item similarity recommender for low‑frequency, highly personalized homestay listings, detailing the data preparation, model architecture, training process, evaluation results, practical improvements, and future directions.

AB testembeddinghomestay

0 likes · 13 min read

Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms