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May 9, 2026 · Artificial Intelligence

NOSE: Enabling AI to Smell with a Unified Molecule‑Receptor‑Semantic Tri‑modal Representation

NOSE introduces a neural olfactory‑semantic embedding that unifies molecular structure, receptor sequences, and natural‑language odor descriptions into a continuous space, achieving state‑of‑the‑art results on eleven tasks and strong zero‑shot generalization for odor and receptor retrieval.

Deep Learningcontrastive learningmolecular design

0 likes · 8 min read

NOSE: Enabling AI to Smell with a Unified Molecule‑Receptor‑Semantic Tri‑modal Representation

AI Algorithm Path

Feb 17, 2026 · Artificial Intelligence

Why Contrastive Learning Is the Core Foundation of Visual Language Models

The article explains how contrastive learning replaces fixed‑category visual training with a relationship‑based approach, detailing the dual‑encoder architecture, cosine similarity loss, batch scaling, temperature control, zero‑shot capabilities, scalability from web data, and the method's strengths and limitations in modern multimodal AI.

CLIPMultimodal AIVisual-Language Models

0 likes · 25 min read

Why Contrastive Learning Is the Core Foundation of Visual Language Models

AntTech

Feb 5, 2026 · Artificial Intelligence

How Triple Alignment and Rationale Generation Supercharge Knowledge‑Based VQA

This paper presents a lightweight, high‑efficiency framework called Triple Alignment with Rationale Generation (TAG) that transforms knowledge‑based visual question answering into a contrastive learning task, dramatically reducing trainable parameters while achieving state‑of‑the‑art performance on major KVQA benchmarks.

CLIPMultimodalVQA

0 likes · 7 min read

How Triple Alignment and Rationale Generation Supercharge Knowledge‑Based VQA

PaperAgent

Feb 3, 2026 · Artificial Intelligence

Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph

Relink introduces a ‘reason‑and‑construct’ paradigm that builds knowledge‑graph paths during inference, combining a high‑precision factual graph with a high‑recall potential‑relation pool, using query‑driven dynamic path expansion and contrastive alignment to markedly improve multi‑hop QA performance and robustness to sparse knowledge.

Dynamic RetrievalGraphRAGLLM

0 likes · 8 min read

Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph

PaperAgent

Jan 27, 2026 · Artificial Intelligence

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

This article analyzes the Agentic‑R framework, which upgrades traditional single‑hop Retrieval‑Augmented Generation by introducing dual‑perspective scoring and a bidirectional flywheel, resulting in 2–3 absolute EM improvements across seven QA datasets and a 10–15% reduction in search rounds.

LLMRAGagentic search

0 likes · 6 min read

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

DeWu Technology

Jan 21, 2026 · Artificial Intelligence

Breaking the Recommendation Feedback Loop with LLM‑Powered Dynamic User Knowledge Graphs

By integrating large language models to dynamically construct user knowledge graphs and applying two‑hop reasoning, the authors enhance serendipity in a large‑scale e‑commerce community recommendation system, achieving significant online gains in diversity, novelty, and user engagement metrics.

Industrial DeploymentLLMSerendipity

0 likes · 17 min read

Breaking the Recommendation Feedback Loop with LLM‑Powered Dynamic User Knowledge Graphs

PaperAgent

Jan 13, 2026 · Artificial Intelligence

How C2LLM Redefines Code Retrieval with Attention‑Based Pooling

Introducing C2LLM, a contrastive code LLM series that replaces mean and EOS pooling with a multi‑head attention pooling module, achieving top scores on the MTEB‑Code benchmark across 12 tasks and demonstrating cost‑effective, high‑precision code retrieval for both production and AI agent applications.

MTEB-CodeRetrieval Augmented Generationattention pooling

0 likes · 8 min read

How C2LLM Redefines Code Retrieval with Attention‑Based Pooling

Kuaishou Tech

Dec 18, 2025 · Artificial Intelligence

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

The paper introduces SSR, a novel multimodal recommendation framework that leverages graph Fourier transforms, energy‑balanced frequency bands, structured regularization, and low‑rank tensor decomposition to replace black‑box fusion with explainable, adaptive reasoning, achieving state‑of‑the‑art results on Amazon datasets and strong cold‑start performance.

cold startcontrastive learningfrequency domain

0 likes · 15 min read

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

Data Party THU

Nov 22, 2025 · Artificial Intelligence

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

FreRA introduces a lightweight, plug‑in frequency‑refined augmentation that adaptively refines spectral components to preserve global semantics while injecting variance, dramatically improving contrastive learning performance on time‑series classification, anomaly detection, and transfer learning across multiple benchmark datasets.

Time Seriescontrastive learningdata augmentation

0 likes · 13 min read

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

DataFunSummit

Oct 12, 2025 · Artificial Intelligence

How Baidu’s Generative Recall System (COBRA) Revolutionizes Ad Recommendations

This article details Baidu's generative recommendation ad recall framework, introducing the COBRA system and its three development stages—dense representation compression, sparse quantization with ID generation, and dense‑sparse cascading—highlighting coarse‑to‑fine inference, performance gains, long‑sequence extensions, online deployment, and future research directions.

COBRAad recallcontrastive learning

0 likes · 18 min read

How Baidu’s Generative Recall System (COBRA) Revolutionizes Ad Recommendations

AI Frontier Lectures

Jul 10, 2025 · Artificial Intelligence

Can Dispersive Loss Supercharge Diffusion Models Without Extra Pre‑training?

Dispersive Loss is a plug‑and‑play regularization technique that enhances diffusion‑based generative models by encouraging dispersed internal representations, requiring no additional pre‑training, parameters, or data, and consistently improves performance across various model sizes and configurations, as demonstrated through extensive experiments.

Dispersive LossModel EvaluationRegularization

0 likes · 18 min read

Can Dispersive Loss Supercharge Diffusion Models Without Extra Pre‑training?

AI Algorithm Path

Jul 5, 2025 · Artificial Intelligence

Beginner’s Guide to Vision‑Language Models Day 7: How CLIP Achieves Joint Visual‑Language Understanding

This article explains CLIP’s dual‑encoder architecture—using a Vision Transformer for images and a Transformer for text—how both encoders map inputs into a shared embedding space, the role of cosine similarity, and the InfoNCE contrastive loss that drives joint visual‑language learning.

CLIPInfoNCEMulti-modal Embedding

0 likes · 8 min read

Beginner’s Guide to Vision‑Language Models Day 7: How CLIP Achieves Joint Visual‑Language Understanding

AI Algorithm Path

Jun 23, 2025 · Artificial Intelligence

Visual Language Model Beginner’s Guide Day 4: Major Contrastive Learning Frameworks

This article surveys six leading contrastive learning frameworks—SimCLR, MoCo, BYOL, SwAV, Barlow Twins, and NNCLR—detailing their loss functions, data‑augmentation pipelines, encoder architectures, and unique mechanisms such as momentum queues, twin networks, clustering swaps, and redundancy reduction, while highlighting their advantages and impact on self‑supervised vision research.

BYOLBarlow TwinsMoCo

0 likes · 14 min read

Visual Language Model Beginner’s Guide Day 4: Major Contrastive Learning Frameworks

AI Algorithm Path

Jun 22, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions

This article systematically introduces the most common contrastive learning loss functions—including Contrastive Loss, Triplet Loss, N‑pair Loss, InfoNCE, and Cross‑Entropy—explaining their mathematical formulations, advantages, challenges, and typical applications in visual, textual, and multimodal representation learning.

InfoNCELoss FunctionsVisual-Language Models

0 likes · 10 min read

Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions

AI Algorithm Path

Jun 20, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

This article explains contrastive learning for visual language models, covering its definition, four‑step workflow, how to choose positive and negative pairs, the difference between supervised and self‑supervised variants, and why the technique is essential for zero‑shot and cross‑modal capabilities.

Visual-Language Modelscontrastive learningdata augmentation

0 likes · 6 min read

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

AI Algorithm Path

Jun 20, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 1: What They Are and Why They Matter

This article introduces visual‑language models (VLMs), explaining how they combine large language models with visual encoders, why they overcome the rigidity of traditional computer‑vision systems, their key advantages, modular architecture, training methods, and practical applications such as image captioning and visual question answering.

AI applicationsComputer VisionMultimodal AI

0 likes · 8 min read

Beginner’s Guide to Visual Language Models – Day 1: What They Are and Why They Matter

AI Algorithm Path

Jun 15, 2025 · Artificial Intelligence

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

This article explains why generic text‑embedding models often fail in specialized retrieval tasks, then demonstrates how to fine‑tune such models using contrastive learning, curated job‑listing data, and the Sentence‑Transformers library, achieving near‑perfect accuracy on a job‑matching benchmark.

Fine-tuningSentence-Transformerscontrastive learning

0 likes · 11 min read

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

Network Intelligence Research Center (NIRC)

May 14, 2025 · Artificial Intelligence

Hands‑On CLIP: Implementing Multimodal Vision‑Language Understanding

This article introduces OpenAI’s CLIP multimodal model, explains its architecture and contrastive training, details hardware and installation steps, and demonstrates a hands‑on zero‑shot image classification workflow that achieves 97% confidence on a cat image without any task‑specific fine‑tuning.

CLIPMultimodalPython

0 likes · 6 min read

Hands‑On CLIP: Implementing Multimodal Vision‑Language Understanding

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

Baidu Geek Talk

Mar 12, 2025 · Artificial Intelligence

How LLMs Are Revolutionizing Semantic Embeddings: Models, Methods, and Trends

This article reviews how large language models (LLMs) enhance semantic text embeddings by comparing traditional methods with LLM‑based approaches, detailing synthetic data generation, backbone model designs, key model families, experimental results on the MTEB benchmark, and future research challenges.

LLMcontrastive learningmodel comparison

0 likes · 30 min read

How LLMs Are Revolutionizing Semantic Embeddings: Models, Methods, and Trends

DeWu Technology

Feb 19, 2025 · Artificial Intelligence

Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN

The paper presents a comprehensive multi‑scenario recommendation study introducing three models—SACN, SAINet, and DSWIN—that integrate scene‑aware attention, attribute‑level preferences, and contrastive disentanglement to capture distinct user interests, achieving consistent AUC gains and online CTR improvements across real‑world datasets.

CTR predictionDeep Learningcontrastive learning

0 likes · 43 min read

Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN

Xiaohongshu Tech REDtech

Dec 26, 2024 · Artificial Intelligence

Instruction Embedding: Latent Representations of Instructions for Task Identification

The paper introduces Instruction Embedding—a task‑focused text representation learned on the new Instruction Embedding Benchmark—and shows that Prompt‑based Instruction Embedding (PIE) outperforms standard embeddings in clustering, similarity, and downstream tasks such as data selection, in‑context example retrieval, test‑set compression, and task‑correlation analysis.

Fine-tuningcontrastive learninginstruction embedding

0 likes · 15 min read

Instruction Embedding: Latent Representations of Instructions for Task Identification

Baobao Algorithm Notes

Oct 24, 2024 · Artificial Intelligence

How NoteLLM-2 Boosts Multimodal Recommendations with In-Content Learning

NoteLLM-2 introduces multimodal In-Content Learning and Late Fusion to overcome visual‑modality bias in end‑to‑end fine‑tuned large representation models, delivering significant gains over baseline multimodal LLMs and traditional retrieval methods in recommendation tasks.

AI researchMultimodal LLMcontrastive learning

0 likes · 11 min read

How NoteLLM-2 Boosts Multimodal Recommendations with In-Content Learning

DataFunTalk

Aug 5, 2024 · Artificial Intelligence

Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches, and Insights

This article presents a comprehensive study on integrating multimodal image‑text representations into large‑scale e‑commerce advertising CTR models, introducing a semantic‑aware contrastive pre‑training (SCL) method and two application algorithms (SimTier and MAKE) that together achieve over 1 % GAUC improvement and significant online gains.

CTR predictioncontrastive learninge‑commerce

0 likes · 21 min read

Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches, and Insights

Alimama Tech

Aug 2, 2024 · Artificial Intelligence

Multimodal Representations Boost Taobao Display Advertising CTR

Alibaba’s advertising team introduces semantic‑aware contrastive learning to pre‑train multimodal image‑text embeddings, integrates them via SimTier and MAKE into ID‑based CTR models, achieving up to 6.9% lift in Taobao display ad click‑through rates and improving long‑tail item performance.

CTR predictionMultimodal Learningcontrastive learning

0 likes · 21 min read

Multimodal Representations Boost Taobao Display Advertising CTR

NewBeeNLP

Jun 20, 2024 · Artificial Intelligence

How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper

This article analyzes Kuaishou’s May 2024 paper on LLM‑driven recommendation, detailing its dual‑tower architecture, contrastive learning of user and item embeddings, and a CVR‑auxiliary task that together improve cold‑start handling and boost both offline and online AUC metrics.

Industrial ApplicationItem EmbeddingLLM

0 likes · 10 min read

How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper

Alibaba Cloud Big Data AI Platform

May 29, 2024 · Artificial Intelligence

ContraLSP: Contrastive Sparse Perturbations Transform Time‑Series Explanation

Recent collaboration between Alibaba Cloud’s big‑data team and leading universities introduced ContraLSP, a novel contrastive and locally sparse perturbation framework that outperforms state‑of‑the‑art methods in explaining time‑series models, offering improved interpretability for both white‑box forecasting and black‑box classification tasks.

Interpretabilitycontrastive learningmachine learning

0 likes · 8 min read

ContraLSP: Contrastive Sparse Perturbations Transform Time‑Series Explanation

Alibaba Cloud Big Data AI Platform

Dec 11, 2023 · Artificial Intelligence

How Hyperbolic Space and Contrastive Learning Boost Domain-Specific Language Models

This article introduces the KANGAROO model, which injects hierarchical semantic information via hyperbolic space and leverages contrastive learning on dense subgraph structures to overcome global sparsity in vertical‑domain knowledge‑enhanced pre‑trained language models, and evaluates its performance on finance and medical tasks.

NLPcontrastive learningdomain adaptation

0 likes · 10 min read

How Hyperbolic Space and Contrastive Learning Boost Domain-Specific Language Models

DataFunSummit

Dec 8, 2023 · Artificial Intelligence

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

This article presents NetEase Cloud Music's multimodal cold‑start solution, detailing the problem background, feature selection using CLIP, two modeling approaches (I2I2U indirect and U2I DSSM direct), contrastive learning enhancements, interest‑boundary modeling, and evaluation results showing significant gains in user engagement.

AIMultimodalcold-start

0 likes · 15 min read

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

Alimama Tech

Nov 15, 2023 · Artificial Intelligence

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.

ad rankingcontrastive learningmachine learning

0 likes · 16 min read

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

DataFunTalk

Nov 10, 2023 · Artificial Intelligence

Multimodal Cold-Start Techniques for Music Recommendation at NetEase Cloud Music

This article presents NetEase Cloud Music's multimodal cold-start recommendation approach, detailing the problem's significance, feature extraction using CLIP, I2I2U indirect modeling, U2I DSSM direct modeling with contrastive learning and interest‑boundary mechanisms, deployment pipeline, evaluation results, and future optimization directions.

Multimodal Learningcold startcontrastive learning

0 likes · 14 min read

NetEase Media Technology Team

Nov 6, 2023 · Artificial Intelligence

Overview of Sequential Recommendation Models

The article surveys sequential recommendation models from early non-deep approaches like FPMC, through RNN-based GRU4Rec and CNN-based Caser, to Transformer-based methods such as SASRec, BERT4Rec, TiSASRec, and recent contrastive-learning techniques, recommending SASRec or its variants for production use.

Deep LearningTransformercontrastive learning

0 likes · 17 min read

Overview of Sequential Recommendation Models

Alimama Tech

Nov 1, 2023 · Artificial Intelligence

BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network

BOMGraph introduces a unified heterogeneous graph neural network that jointly models text, image, and similar‑item search across multiple e‑commerce scenarios, using meta‑path‑guided attention, disentangled scenario‑specific and shared embeddings, and contrastive learning to alleviate sample sparsity, achieving consistent offline and online performance gains.

Graph Neural Networkcontrastive learninge‑commerce

0 likes · 13 min read

BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network

Kuaishou Tech

Sep 26, 2023 · Artificial Intelligence

Cross-Domain Product Representation (COPE): A Large-Scale Dataset and Baseline Model for Rich‑Content E‑Commerce

The paper introduces ROPE, the first large‑scale cross‑domain product recognition dataset covering detail pages, short videos and live streams, and proposes COPE, a dual‑tower multimodal model that learns unified product embeddings using contrastive and classification losses, achieving superior retrieval and few‑shot classification performance across domains.

DatasetDeep Learningcontrastive learning

0 likes · 13 min read

Cross-Domain Product Representation (COPE): A Large-Scale Dataset and Baseline Model for Rich‑Content E‑Commerce

Alimama Tech

Sep 12, 2023 · Artificial Intelligence

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

CC‑GNN addresses three drawbacks of existing graph‑neural retrieval for e‑commerce by adding content phrase nodes, scalable meta‑path message passing, and difficulty‑aware noisy contrastive learning with counterfactual augmentation, achieving up to 16 % recall improvement and notably larger gains on long‑tail queries and cold‑start items.

E-commerce SearchLong Tailcold start

0 likes · 19 min read

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

Huolala Tech

Jul 28, 2023 · Artificial Intelligence

How HuoLala Leverages AI to Revolutionize Service Quality Inspection

This article details HuoLala's AI‑driven intelligent quality inspection system, covering its NLP‑based semantic understanding pipeline, data denoising, confidence learning, contrastive learning, model acceleration techniques such as pruning, knowledge distillation, quantization, and interpretability methods to improve coverage, recall and risk detection.

NLPcontrastive learningdata denoising

0 likes · 23 min read

How HuoLala Leverages AI to Revolutionize Service Quality Inspection

DataFunTalk

Jun 21, 2023 · Artificial Intelligence

Low‑Resource NLP Pretraining: Methodology, Experiments, and Zero‑Shot Applications

This article presents a low‑resource NLP pretraining approach that combines transformer‑based language modeling with contrastive vector learning, details the unsupervised sample‑pair construction, introduces a camel‑shaped masking distribution, and demonstrates through extensive experiments that the resulting model achieves strong zero‑shot NLU, NLG, and retrieval performance while requiring minimal compute and data.

Language ModelingLow-Resourcecontrastive learning

0 likes · 10 min read

Low‑Resource NLP Pretraining: Methodology, Experiments, and Zero‑Shot Applications

Network Intelligence Research Center (NIRC)

May 24, 2023 · Artificial Intelligence

COPNER: Contrastive Learning with Prompt Guidance for Few‑Shot Named Entity Recognition

The article introduces COPNER, a contrastive‑learning framework that uses class‑specific prompt words to guide sentence encoders, addressing the limited semantic capture of existing few‑shot NER methods and demonstrating superior performance across multiple benchmark datasets and K‑shot settings.

COPNERNLPcontrastive learning

0 likes · 4 min read

COPNER: Contrastive Learning with Prompt Guidance for Few‑Shot Named Entity Recognition

DataFunSummit

May 23, 2023 · Artificial Intelligence

Continuous Semantic Enhancement for Neural Machine Translation: Methodology, Experiments, and Community Deployment

This article introduces a continuous semantic enhancement approach for neural machine translation that overcomes the limitations of discrete data‑augmentation techniques, details the neighbor risk minimization training objective, presents benchmark improvements on ACL‑2022 datasets, and describes practical deployment and fine‑tuning workflows in the Modu community.

Neural Machine Translationcontinuous semantic augmentationcontrastive learning

0 likes · 19 min read

Continuous Semantic Enhancement for Neural Machine Translation: Methodology, Experiments, and Community Deployment

Network Intelligence Research Center (NIRC)

May 13, 2023 · Artificial Intelligence

A Quick Survey of Recent Time Series Representation Learning Papers

This article reviews the importance and challenges of time‑series data, then summarizes recent contrastive‑learning approaches (TS2Vec, TF‑C) and mask‑reconstruction methods (PatchTST), highlighting their designs, key mechanisms, and future research directions.

PatchTSTTS2VecTime Series

0 likes · 7 min read

A Quick Survey of Recent Time Series Representation Learning Papers

AntTech

May 10, 2023 · Artificial Intelligence

Brainwave and Behavior Recognition: Multi‑Modal Biometric Authentication with Adversarial Contrastive Transfer Learning

This article presents Ant Security's research on novel biometric methods—brainwave (脑纹) and behavior recognition—detailing their scientific background, data collection, multi‑modal deep‑learning algorithms, adversarial and contrastive training strategies, experimental results, and practical applications for inclusive, secure identity verification.

Multimodal AIaccessibilityadversarial learning

0 likes · 17 min read

Brainwave and Behavior Recognition: Multi‑Modal Biometric Authentication with Adversarial Contrastive Transfer Learning

Kuaishou Tech

Apr 25, 2023 · Artificial Intelligence

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

The paper introduces DCCL, a model‑agnostic contrastive learning framework that decouples user interest and conformity representations to address popularity bias and out‑of‑distribution challenges in recommendation systems, demonstrating significant offline and online performance gains on real‑world datasets.

OOD robustnesscausal inferencecontrastive learning

0 likes · 8 min read

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

Alimama Tech

Dec 14, 2022 · Artificial Intelligence

Contrastive Image Representation Learning with Debiasing for CTR Prediction

The article proposes a three-stage contrastive learning framework—pre‑training, fine‑tuning, and debiasing—to generate unbiased, fine‑grained image embeddings for mobile Taobao CTR prediction, achieving higher accuracy, fairness, and a 4‑5% CTR lift in large‑scale offline and online evaluations.

CTR predictionDeep Learningbias mitigation

0 likes · 14 min read

Contrastive Image Representation Learning with Debiasing for CTR Prediction

Alibaba Cloud Big Data AI Platform

Dec 8, 2022 · Artificial Intelligence

KECP: Enhancing Few-Shot Machine Reading Comprehension via Knowledge-Driven Prompt Tuning

KECP, a Knowledge‑Enhanced Contrastive Prompt‑tuning model, achieves strong few‑shot extractive question answering by converting questions to masked statements, injecting external knowledge via gated fusion, and leveraging contrastive learning alongside masked language modeling, as demonstrated on EMNLP‑2022 benchmarks.

NLPcontrastive learningknowledge injection

0 likes · 9 min read

KECP: Enhancing Few-Shot Machine Reading Comprehension via Knowledge-Driven Prompt Tuning

Baidu Geek Talk

Nov 16, 2022 · Artificial Intelligence

How Baidu’s Ernie‑SimCSE Uses Contrastive Learning to Crush Spam Promotion

This article explains how Baidu's anti‑spam team tackled large‑scale promotional spam on Baidu Zhidao by combining the Ernie pretrained model with SimCSE contrastive learning, detailing the problem background, traditional methods, text‑representation stages, the SimCSE approach, training pipeline, optimizations, and experimental results.

ErnieNLPSimCSE

0 likes · 15 min read

How Baidu’s Ernie‑SimCSE Uses Contrastive Learning to Crush Spam Promotion

Alimama Tech

Nov 9, 2022 · Artificial Intelligence

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

The paper introduces a graph‑based weakly supervised contrastive learning framework that uses heterogeneous user‑behavior graphs, e‑commerce‑specific augmentations, and a hybrid fine‑tuning/transfer learning strategy to improve semantic relevance matching between queries and product titles, achieving significant gains on a large‑scale Taobao dataset.

Weak Supervisioncontrastive learninge‑commerce

0 likes · 12 min read

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

Zhuanzhuan Tech

Oct 28, 2022 · Artificial Intelligence

Contrastive Learning: Definitions, Principles, Classic Algorithms, and Applications in Recommendation Systems

This article introduces contrastive learning, explains its definition, principles, and classic algorithms such as SimCLR and MoCo, and details its practical applications in recommendation systems, including a case study of its deployment at Zhuanzhuan that boosted order rates by over 10%.

AIcontrastive learningself-supervised learning

0 likes · 12 min read

Contrastive Learning: Definitions, Principles, Classic Algorithms, and Applications in Recommendation Systems

Youzan Coder

Oct 24, 2022 · Artificial Intelligence

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

The article outlines a comprehensive knowledge‑base retrieval matching solution—combining PageRank‑enhanced DSL rewriting, keyword and dual‑tower vector recall, contrastive fine‑ranking, and optimized vector‑based ranking—implemented via offline DP training and Sunfish online inference on Milvus, with applications in enterprise search and recommendations and future plans for graph‑neural embeddings.

InfoNCEMilvusNLP

0 likes · 12 min read

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

DataFunTalk

Oct 6, 2022 · Information Security

Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions

This article presents a comprehensive overview of applying graph machine learning to security risk control, covering background cases, system architecture, dynamic heterogeneous graph modeling with HGT and DDGCL, experimental results, and future research directions for fraud, money‑laundering, and gambling detection.

contrastive learningdynamic heterogeneous graphfinancial fraud detection

0 likes · 10 min read

Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions

DataFunTalk

Sep 27, 2022 · Artificial Intelligence

Contrastive Learning for Text Generation: Motivation, Methodology, Experiments, and Discussion (CoNT Framework)

This article reviews the integration of contrastive learning into text generation, explains why it helps mitigate exposure bias, introduces the CoNT framework with three key improvements, presents extensive experiments on translation, summarization, code comment and data‑to‑text tasks, and discusses practical deployment considerations.

AICoNTText Generation

0 likes · 21 min read

Contrastive Learning for Text Generation: Motivation, Methodology, Experiments, and Discussion (CoNT Framework)

DataFunSummit

Sep 6, 2022 · Artificial Intelligence

Recent Advances in Self‑Supervised Learning for Text Recognition (OCR)

This article reviews recent progress in applying self‑supervised learning to OCR text recognition, covering mainstream model architectures, key considerations for self‑supervised tasks on text images, and detailed analyses of representative papers such as SeqCLR, SimAN, and DiG, highlighting their designs, experiments, and results.

Computer VisionOCRcontrastive learning

0 likes · 20 min read

Recent Advances in Self‑Supervised Learning for Text Recognition (OCR)

Laiye Technology Team

Aug 15, 2022 · Artificial Intelligence

Recent Advances in Self‑Supervised Learning for Text Recognition

This article reviews recent self‑supervised learning approaches for optical character recognition, covering mainstream OCR model architectures, key factors for applying contrastive and masked image modeling methods to text images, and detailed analyses of representative works such as SeqCLR, SimAN, and DiG, including their designs and experimental results.

OCRcontrastive learningmasked image modeling

0 likes · 19 min read

Recent Advances in Self‑Supervised Learning for Text Recognition

Youzan Coder

Jul 11, 2022 · Artificial Intelligence

How Contrastive Learning Revolutionizes Product Term Prediction in E‑commerce

By leveraging contrastive learning and large‑scale click‑through data, the article details a dual‑tower model that encodes product titles and queries, explains loss functions, batch‑negative sampling, distributed training tricks, and demonstrates how this approach outperforms traditional NER for product term and category prediction.

Distributed TrainingE-commerce AIInfoNCE

0 likes · 16 min read

How Contrastive Learning Revolutionizes Product Term Prediction in E‑commerce

DataFunSummit

Jun 25, 2022 · Artificial Intelligence

Image and Text Pretraining: Methods, Practices, and Business Applications in Information Flow

This article reviews large‑scale image and multimodal pre‑training techniques—including contrastive learning, self‑supervised reconstruction, and multimodal alignment—explains data acquisition, model construction, evaluation metrics, and demonstrates how these methods are applied and optimized for real‑world information‑flow services.

AIInformation Flowcontrastive learning

0 likes · 17 min read

Image and Text Pretraining: Methods, Practices, and Business Applications in Information Flow

Alimama Tech

Jun 15, 2022 · Artificial Intelligence

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

The paper introduces MUVCOG, a heterogeneous graph neural network that models multi‑modal, multi‑query search sessions on Mobile Taobao by jointly learning attention‑based global and hierarchical local views through contrastive pre‑training, yielding universal session embeddings that markedly improve CTR prediction, query recommendation, and intent classification.

Graph Neural Networkcontrastive learningmulti-modal

0 likes · 15 min read

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

Alimama Tech

Jun 8, 2022 · Artificial Intelligence

CTR-Driven Advertising Text Generation and Bundle Creative Optimization (CREATER & CONNA)

Alibaba’s advertising team introduces CREATER, a CTR‑driven text generator that leverages user reviews, aspect control codes, and contrastive fine‑tuning, and CONNA, a non‑autoregressive bundle creator that predicts heterogeneous ad elements with set‑based loss, both delivering substantial online CTR gains and CPC reductions through dynamic creative optimization.

CTRDynamic creative optimizationNLP

0 likes · 25 min read

CTR-Driven Advertising Text Generation and Bundle Creative Optimization (CREATER & CONNA)

DaTaobao Tech

May 31, 2022 · Artificial Intelligence

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

The paper proposes CDAN, a dual‑tower retrieval model that separates item attribute and popularity representations via a Feature Decoupling Module with orthogonal embeddings, aligns head‑tail attribute distributions using MMD and contrastive learning, and jointly trains biased and unbiased towers, achieving higher tail recall, lower exposure concentration, and measurable online click‑through improvements.

contrastive learningdomain adaptationdual-tower model

0 likes · 13 min read

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

DataFunSummit

May 26, 2022 · Artificial Intelligence

Exploring Contrastive Learning in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how contrastive learning can alleviate data sparsity and distribution bias in recommendation systems, detailing its theoretical advantages, recent research progress in computer vision and NLP, and a multi‑task self‑supervised framework applied to Kuaishou's short‑video ranking pipeline with significant offline and online performance gains.

AIKuaishoubias mitigation

0 likes · 21 min read

Exploring Contrastive Learning in Kuaishou Recommendation Systems

DaTaobao Tech

May 17, 2022 · Artificial Intelligence

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

The paper demonstrates how self‑supervised models SwAV and M6 generate high‑quality image and multimodal embeddings for Meiping Meiwu’s recommendation system, delivering notable gains in scene/style consistency, ranking AUC, classification and retrieval performance, especially for cold‑start items, and achieving measurable production lifts.

A/B testingM6 multimodalSwAV

0 likes · 15 min read

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

Bilibili Tech

May 10, 2022 · Artificial Intelligence

Glance Supervised Video Moment Retrieval via the ViGA Framework

The paper presents a glance‑supervised video moment retrieval approach that records a single annotator‑seen frame, introduces the ViGA contrastive learning framework to leverage this weak temporal cue, and demonstrates on three benchmarks performance rivaling fully supervised methods while keeping annotation cost minimal.

Computer VisionGlance SupervisionMultimodal

0 likes · 8 min read

Glance Supervised Video Moment Retrieval via the ViGA Framework

Kuaishou Tech

Apr 18, 2022 · Artificial Intelligence

SSAN: A Novel Dual‑Stream Network for Domain‑Generalized Face Anti‑Spoofing

This paper proposes SSAN, a novel dual‑stream network that separates content and style features to achieve domain‑generalized face anti‑spoofing, employing adversarial learning for content, contrastive learning for style, and a large‑scale evaluation protocol across twelve public datasets, achieving state‑of‑the‑art performance.

SSANStyle Transfercontrastive learning

0 likes · 16 min read

SSAN: A Novel Dual‑Stream Network for Domain‑Generalized Face Anti‑Spoofing

DaTaobao Tech

Apr 12, 2022 · Artificial Intelligence

ArcCSE: Angular Margin Contrastive Learning for Self‑Supervised Text Representation

ArcCSE introduces an angular‑margin contrastive loss and both pairwise (dropout‑augmented) and triple‑wise (span‑masked) relationship modeling to self‑supervise text embeddings, yielding tighter decision boundaries, higher alignment and uniformity, and superior performance on unsupervised STS, SentEval, and Alibaba’s retrieval and recommendation systems.

NLPangular margincontrastive learning

0 likes · 8 min read

ArcCSE: Angular Margin Contrastive Learning for Self‑Supervised Text Representation

Laiye Technology Team

Apr 1, 2022 · Artificial Intelligence

Self‑Supervised Learning: Contrastive Methods and the MoCo Series (V1‑V3)

This article introduces the four types of machine learning, explains self‑supervised learning, details generative and contrastive approaches, and provides an in‑depth overview of the MoCo series (V1‑V3), including their architectures, training strategies, and experimental results on document image classification and text‑line detection tasks.

MoCoVision Transformerscontrastive learning

0 likes · 12 min read

Self‑Supervised Learning: Contrastive Methods and the MoCo Series (V1‑V3)

Baobao Algorithm Notes

Mar 7, 2022 · Artificial Intelligence

How CLIP Uses Natural Language Supervision for Powerful Zero‑Shot Vision

This article explains CLIP’s multimodal contrastive pre‑training, its simple yet effective architecture, code implementation, and how its zero‑shot capability can surpass supervised ImageNet models by leveraging a 400‑million image‑text dataset and shared semantic embeddings.

AICLIPMultimodal

0 likes · 7 min read

How CLIP Uses Natural Language Supervision for Powerful Zero‑Shot Vision

DataFunSummit

Feb 12, 2022 · Artificial Intelligence

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

After the BERT era, this article reviews the limitations of pre‑trained language models for semantic matching, discusses negative‑sample sampling, data‑augmentation techniques, contrastive learning methods such as ConSERT and SimCSE, and practical deployment considerations in vector‑based retrieval systems.

contrastive learningdata augmentationpretrained language models

0 likes · 20 min read

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

DataFunSummit

Jan 8, 2022 · Artificial Intelligence

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve graph neural network performance without requiring task labels.

Graph RepresentationRobustnessUnsupervised Learning

0 likes · 15 min read

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

Code DAO

Dec 22, 2021 · Artificial Intelligence

Understanding SimCLR: A Simple Contrastive Learning Framework for Visual Representations

This article explains SimCLR, the 2020 Google Research framework that advances self‑supervised visual pre‑training by using extensive data augmentations, a ResNet encoder, a projection‑head MLP, and the NT‑Xent loss to learn robust image representations that outperform many prior methods on ImageNet and other benchmarks.

Computer VisionNT-Xent lossResNet

0 likes · 7 min read

Understanding SimCLR: A Simple Contrastive Learning Framework for Visual Representations

Baobao Algorithm Notes

Dec 16, 2021 · Artificial Intelligence

Boost Model Robustness with 5 Lines of R‑Drop Contrastive Learning

This article introduces a simple five‑line implementation of R‑Drop, a contrastive self‑supervised learning technique that leverages dropout‑induced perturbations to improve model robustness, explains the underlying principle, provides the exact PyTorch code, and compares it with the ConSERT method.

DropoutPyTorchcontrastive learning

0 likes · 5 min read

Boost Model Robustness with 5 Lines of R‑Drop Contrastive Learning

DataFunTalk

Dec 11, 2021 · Artificial Intelligence

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve robustness and performance without requiring task labels.

RobustnessUnsupervised Learningcontrastive learning

0 likes · 16 min read

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

DataFunTalk

Nov 1, 2021 · Artificial Intelligence

Reflections on Working as an Algorithm Engineer at Meituan and the Rise of Contrastive Learning

The author shares personal experiences as a Meituan algorithm engineer, emphasizing the critical role of labeled data, the emergence of contrastive (self‑supervised) learning across computer vision, NLP, and recommendation systems, and offers practical advice for algorithm engineers to stay competitive.

AI researchMeituanalgorithm engineering

0 likes · 8 min read

Reflections on Working as an Algorithm Engineer at Meituan and the Rise of Contrastive Learning

DataFunSummit

Oct 29, 2021 · Artificial Intelligence

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

This talk explains contrastive learning fundamentals, typical image‑domain models such as SimCLR, MoCo and SwAV, and shows how their principles—positive/negative sample construction, encoder design, loss functions, alignment and uniformity—can be applied to improve dual‑tower retrieval and ranking models, embedding normalization, temperature scaling, and graph‑based recommender systems.

InfoNCEcontrastive learningdual-tower models

0 likes · 40 min read

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

DataFunTalk

Oct 26, 2021 · Artificial Intelligence

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

This article explains how contrastive learning, originally popular in computer‑vision, can be interpreted and applied to recommendation‑system recall and coarse‑ranking models, covering its theoretical roots, typical architectures like SimCLR, MoCo and SwAV, and practical tricks such as in‑batch negatives, embedding normalization, temperature scaling, and graph‑based extensions.

contrastive learningdual-tower modelsembedding normalization

0 likes · 40 min read

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

Kuaishou Tech

Oct 20, 2021 · Artificial Intelligence

HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval

This paper proposes HiT, a hierarchical transformer model with momentum contrast for video-text retrieval, addressing limitations in existing multimodal learning methods by introducing hierarchical cross-modal contrast matching and momentum cross-modal contrast to improve retrieval performance.

HCMMCCMoCo

0 likes · 9 min read

HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval

DataFunTalk

Oct 19, 2021 · Artificial Intelligence

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

This article reviews recent research on graph self‑supervised learning, focusing on contrastive learning fundamentals, the SimCLR‑style framework, representative models such as GRACE and its adaptive augmentation extension GCA, experimental evaluations, and future directions for graph contrastive methods.

GCAGraceGraph Representation

0 likes · 16 min read

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

DataFunSummit

Oct 19, 2021 · Artificial Intelligence

Deep Graph Contrastive Learning: GRACE and GCA

This article reviews recent advances in graph contrastive learning, introducing foundational concepts, the SimCLR framework, and representative models such as GRACE and its adaptive augmentation variant GCA, followed by experimental results, analysis, and future research directions.

GCAGraceGraph Representation

0 likes · 16 min read

Deep Graph Contrastive Learning: GRACE and GCA

DataFunTalk

Sep 29, 2021 · Artificial Intelligence

Self‑Supervised Learning and Contrastive Learning for Computer Vision and OCR Applications

This article reviews self‑supervised learning techniques, common computer‑vision pretext tasks, contrastive loss functions, popular frameworks such as SimCLR, MoCo and SimSiam, and demonstrates their application to OCR captcha recognition with detailed implementation and experimental results.

Computer VisionDeep LearningOCR

0 likes · 22 min read

Self‑Supervised Learning and Contrastive Learning for Computer Vision and OCR Applications

DataFunSummit

Sep 26, 2021 · Artificial Intelligence

Contrastive Learning and Its Applications in Weibo Content Representation

This article explains the fundamentals of contrastive learning, reviews typical models such as SimCLR, MoCo, SwAV, BYOL, SimSiam and Barlow Twins, and demonstrates how these methods are applied to Weibo text and multimodal (text‑image) representation tasks like hashtag generation and image‑text matching.

MultimodalNLPWeibo

0 likes · 18 min read

Contrastive Learning and Its Applications in Weibo Content Representation

Laiye Technology Team

Sep 24, 2021 · Artificial Intelligence

Self‑Supervised Learning and Contrastive Methods for Computer Vision and OCR Applications

This article surveys self‑supervised learning techniques for computer‑vision tasks, explains common pretext tasks and contrastive loss designs, reviews representative models such as SimCLR, MoCo, SmAV and SimSiam, and demonstrates their practical impact on a captcha‑OCR system with measurable accuracy gains.

Computer VisionDeep LearningOCR

0 likes · 23 min read

Self‑Supervised Learning and Contrastive Methods for Computer Vision and OCR Applications

DataFunTalk

Aug 30, 2021 · Artificial Intelligence

Contrastive Learning: Foundations, Typical Models, and Applications to Weibo Content Representation

This article explains the concept of contrastive learning, its relationship to self‑supervised and metric learning, describes key system components and loss functions, reviews major image, NLP and multimodal models such as SimCLR, MoCo, SwAV, BYOL, and demonstrates how contrastive learning is applied to Weibo hashtag generation, similar‑post retrieval, and text‑image matching using CD‑TOM and W‑CLIP models.

AIMultimodalWeibo

0 likes · 19 min read

Contrastive Learning: Foundations, Typical Models, and Applications to Weibo Content Representation

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.

BERTInformation SecuritySimCSE

0 likes · 11 min read

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

DataFunTalk

Jun 6, 2021 · Artificial Intelligence

ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT introduces a contrastive self‑supervised framework that enhances BERT‑derived sentence embeddings by applying efficient embedding‑level data augmentations, achieving significant improvements on semantic textual similarity tasks, especially in low‑resource settings, and outperforming previous state‑of‑the‑art methods.

BERTcontrastive learningself-supervised

0 likes · 20 min read

ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

Meituan Technology Team

Jun 3, 2021 · Artificial Intelligence

ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT is a contrastive self‑supervised framework that fine‑tunes BERT with augmented sentence views and NT‑Xent loss to overcome embedding collapse, delivering roughly 8% higher STS performance than prior methods, remaining robust in few‑shot and supervised scenarios, and now deployed in Meituan’s NLP pipelines.

BERTNLPcontrastive learning

0 likes · 20 min read

TiPaiPai Technical Team

May 14, 2021 · Artificial Intelligence

Mastering Text Matching: From SentenceBERT to Contrastive Learning

This article explores the landscape of text matching in NLP, covering problem types, three model interaction levels, sentence embedding techniques, supervised and unsupervised approaches, and the role of contrastive learning with alignment and uniformity metrics.

BERTNLPcontrastive learning

0 likes · 12 min read

Mastering Text Matching: From SentenceBERT to Contrastive Learning

Amap Tech

Dec 30, 2020 · Artificial Intelligence

LRC-BERT: Contrastive Learning based Knowledge Distillation with COS‑NCE Loss for Efficient NLP Models

The Amap team introduced LRC‑BERT, a contrastive‑learning‑based knowledge‑distillation framework that employs a novel COS‑NCE loss, gradient‑perturbation, and a two‑stage training schedule, enabling a 4‑layer student model to retain about 97 % of BERT‑Base accuracy while being 7.5× smaller and 9.6× faster, and it has already improved real‑world traffic‑event extraction performance.

BERTCOS-NCE lossNLP

0 likes · 16 min read

LRC-BERT: Contrastive Learning based Knowledge Distillation with COS‑NCE Loss for Efficient NLP Models