semantic embedding

Baidu Tech Salon

Mar 21, 2025 · Artificial Intelligence

Semantic Embedding with Large Language Models: A Comprehensive Survey

This survey reviews the evolution of semantic embedding—from Word2vec and GloVe to BERT, Sentence‑BERT, and recent contrastive methods—then examines how large language models improve embeddings via synthetic data generation and backbone architectures, detailing techniques such as contrastive prompting, in‑context learning, knowledge distillation, and discussing resource, privacy, and interpretability challenges.

In-Context LearningNLPcontrastive learning

0 likes · 27 min read

Semantic Embedding with Large Language Models: A Comprehensive Survey

58 Tech

Aug 5, 2021 · Artificial Intelligence

Exploration and Practice of Text Representation Algorithms in the 58 Security Scenario

This article presents a comprehensive study of text representation techniques—from weighted word‑vector methods to supervised SimBert and unsupervised contrastive learning models—applied to large‑scale unstructured data in 58's information‑security workflows, evaluating their effectiveness for classification and content‑recall tasks.

BERTSimCSEcontrastive learning

0 likes · 11 min read

Exploration and Practice of Text Representation Algorithms in the 58 Security Scenario

DataFunTalk

May 23, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Based on academic and industry experience, iQIYI has designed a deep semantic representation learning framework that integrates multimodal side information and deep models such as Transformers and graph neural networks, improving recall, ranking, deduplication, diversity and semantic matching across recommendation and search scenarios.

Graph Neural NetworksMultimodalRecommendation systems

0 likes · 27 min read

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

iQIYI Technical Product Team

May 15, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications

iQIYI’s deep semantic representation learning framework integrates multimodal content, knowledge graphs, and user behavior through layered data, feature, strategy, and application components, employing early, late, and hybrid fusion with Transformers, GCNs, and other deep models to deliver high‑quality embeddings that boost recommendation, search, and streaming performance across dozens of business scenarios.

Graph Neural NetworksMultimodalSearch

0 likes · 28 min read

iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications