Word2Vec | BestHub

Rare Earth Juejin Tech Community

Oct 19, 2023 · Artificial Intelligence

NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch

This article introduces word‑level representations—from one‑hot encoding to dense word embeddings via Word2Vec—explains cosine similarity, then walks through the structure, limitations, and PyTorch implementation of a vanilla RNN, including a custom forward function and verification against the library API.

NLPPyTorchRNN

0 likes · 19 min read

NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch

Model Perspective

Aug 10, 2023 · Artificial Intelligence

Understanding Word2Vec: Theory, Architecture, and Python Implementation

This article explains the Word2Vec algorithm, its CBOW and Skip‑Gram architectures, cosine similarity mathematics, training process with negative sampling, and provides a concise Python example using the gensim library.

AINatural Language ProcessingPython

0 likes · 8 min read

Understanding Word2Vec: Theory, Architecture, and Python Implementation

58 Tech

Apr 9, 2021 · Artificial Intelligence

Vectorized Recall and Dual‑Tower Model for Home Page Recommendation at 58.com

This article details how 58.com improved its home‑page recommendation system by introducing vectorized recall with Word2Vec, optimizing negative sampling, deploying FAISS for fast nearest‑neighbor search, and later adopting a dual‑tower deep learning model with user interest features, achieving higher click‑through and conversion rates.

Word2Vecdual‑towerfaiss

0 likes · 19 min read

Vectorized Recall and Dual‑Tower Model for Home Page Recommendation at 58.com

Tencent Advertising Technology

Jul 30, 2020 · Artificial Intelligence

Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM

The article details the Tencent Advertising Algorithm competition final, explains the chizhu team's approach of converting ad IDs into word sequences for text classification using large‑scale word2vec embeddings and a dual BiLSTM architecture, presents custom loss functions, training tricks, and shares full Python model code, achieving an overall rank of 11.

BiLSTMText ClassificationWord2Vec

0 likes · 9 min read

Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM

58 Tech

Jul 10, 2020 · Artificial Intelligence

Tag Mining for Used‑Car Business: NLP, Word2Vec, and Retrieval Pipeline

This article details the end‑to‑end process of extracting and leveraging tags for used‑car listings, covering data collection, segmentation, NLP‑based tokenization, word‑vector generation, tag‑library construction, and online retrieval flow to improve personalized recall and CTR.

NLPTaggingWord2Vec

0 likes · 19 min read

Tag Mining for Used‑Car Business: NLP, Word2Vec, and Retrieval Pipeline

Tencent Advertising Technology

Jun 15, 2020 · Artificial Intelligence

Insights from a Top Contestant on the Tencent Advertising Algorithm Competition: Transformer Modeling and Model Fusion

In this article, a second‑place contestant from Xiamen University shares his practical experience with word2vec‑based sequence models, transformer learning‑rate tuning, handling masked positions in max‑pooling, and techniques for increasing model diversity through input and parameter variations for a large‑scale advertising algorithm competition.

TransformerWord2Vecadvertising

0 likes · 4 min read

Insights from a Top Contestant on the Tencent Advertising Algorithm Competition: Transformer Modeling and Model Fusion

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

Sohu Tech Products

Mar 6, 2019 · Artificial Intelligence

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

This article explains the fundamentals of the Word2Vec SGNS model, details its hyper‑parameters and training tricks, and demonstrates how customized embeddings are built for rental‑listing and news‑article recommendation, covering data preparation, objective‑function redesign, evaluation, and deployment in both recall and ranking stages.

Cold StartSGNSWord2Vec

0 likes · 14 min read

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

Architecture Digest

Feb 22, 2018 · Artificial Intelligence

Deep Learning Applications in Recommendation Systems

This article explains why deep learning has become essential for modern recommendation systems, describing its advantages such as automatic feature extraction, noise robustness, sequential modeling with RNNs, and improved user‑item representation, and reviews major deep‑learning‑based recommendation models and techniques.

Recommendation systemsWord2Vecautoencoders

0 likes · 17 min read

Deep Learning Applications in Recommendation Systems

Qunar Tech Salon

Aug 18, 2016 · Artificial Intelligence

Automatic Ticket Classification Using SVM and word2vec at Qunar

At Qunar, the data center algorithm team developed an automatic ticket classification system that combines Support Vector Machine with word2vec embeddings to handle high‑dimensional, low‑sample text data, achieving 89% accuracy and 80% recall while outlining the full machine‑learning pipeline from feature extraction to deployment.

QunarSVMText Classification

0 likes · 7 min read

Automatic Ticket Classification Using SVM and word2vec at Qunar