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Mar 26, 2026 · Industry Insights

How Alibaba’s Large User Model (LUM) Boosted CTR by 4.5% and Scaled to Billions of Parameters

The article analyzes the evolution from traditional modular recommendation models to a generative Large User Model (LUM), detailing its three‑stage paradigm, tokenization, training objectives, scaling‑law findings, offline and online experiments, and the AI‑infra innovations that enabled a 4.5% CTR lift in production.

CTR predictionGenerative ModelingRecommendation Systems

0 likes · 18 min read

How Alibaba’s Large User Model (LUM) Boosted CTR by 4.5% and Scaled to Billions of Parameters

JD Retail Technology

Dec 11, 2025 · Artificial Intelligence

How GIN-Based Cohort Modeling Boosts Cold-Start CTR Prediction by 2%

This article explains a SIGIR 2025 paper that tackles cold‑start click‑through‑rate prediction in JD's ad system by using a Graph Isomorphism Network‑based cohort modeling framework, detailing its three‑module architecture, extensive experiments on public and industrial datasets, and a live deployment that achieved a 2.13% CTR lift.

CTR predictionGinGraph Neural Network

0 likes · 9 min read

How GIN-Based Cohort Modeling Boosts Cold-Start CTR Prediction by 2%

DataFunSummit

Sep 9, 2025 · Artificial Intelligence

How Baidu’s GRAB Model Uses Scaling Laws to Transform Ad Ranking

This article explains Baidu's generative ranking model GRAB, detailing how scaling laws from large language models inspire a new recommendation paradigm, the model's architecture, custom attention mechanisms, training strategies, deployment optimizations, and the resulting business gains in CTR and revenue.

BaiduCTR predictionRecommendation Systems

0 likes · 22 min read

How Baidu’s GRAB Model Uses Scaling Laws to Transform Ad Ranking

JD Cloud Developers

Jul 18, 2025 · Artificial Intelligence

New Precise Matching Techniques from JD’s SIGIR 2025 Papers

JD's retail technology team presents five SIGIR 2025 papers that introduce advanced graph neural, causal optimal transport, domain‑oriented relevance, multi‑objective bid‑word generation, and hierarchical user behavior models to dramatically improve precise matching in e‑commerce search, recommendation, and advertising.

AdvertisingCTR predictioncausal optimal transport

0 likes · 11 min read

New Precise Matching Techniques from JD’s SIGIR 2025 Papers

JD Retail Technology

Jul 18, 2025 · Artificial Intelligence

How Cutting-Edge AI Models Are Revolutionizing E‑Commerce CTR Prediction

This article showcases five JD Retail Technology research papers accepted at SIGIR 2025, covering graph‑based cohort modeling, causal optimal transport post‑event modeling, an autonomous domain‑oriented relevance engine, a multi‑objective bidword generation model, and hierarchical long‑term user behavior modeling, all advancing e‑commerce CTR prediction and advertising.

CTR predictioncausal optimal transporte-commerce relevance

0 likes · 10 min read

How Cutting-Edge AI Models Are Revolutionizing E‑Commerce CTR Prediction

Alimama Tech

Mar 14, 2025 · Artificial Intelligence

Advances in Search Advertising Models with Large Language Models (2024)

In 2024 Alibaba Mama outlines how large‑language models transform search advertising through a three‑line scaling roadmap—explicit inductive‑bias design, implicit compute growth, and auxiliary CV/NLP advances—implemented via a pre‑train/post‑train/CTR paradigm and the LUM user‑behavior model, promising gains in relevance, recall, and real‑time serving while highlighting inference efficiency challenges.

CTR predictionlarge language modelsmultimodal embedding

0 likes · 25 min read

Advances in Search Advertising Models with Large Language Models (2024)

JD Tech Talk

Mar 13, 2025 · Artificial Intelligence

CTR-Driven Advertising Image Generation with Multimodal Large Language Models

This paper proposes CAIG, a novel method for generating high-CTR advertising images using multimodal large language models, combining reinforcement learning and preference optimization to align generated content with product features.

CTR predictionadvertising image generationmultimodal large language models

0 likes · 10 min read

CTR-Driven Advertising Image Generation with Multimodal Large Language Models

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

JD Retail Technology

Jan 21, 2025 · Artificial Intelligence

Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)

Tech Insight highlights ten 2024 JD Retail Technology AI papers presented at top conferences—including CVPR, SIGIR, WWW, AAAI and IJCAI—that advance open‑vocabulary object detection, unified search‑recommendation, pre‑ranking consistency, diversity‑aware re‑ranking, a diversified product‑search dataset, graph‑based query classification, plug‑in CTR models, parallel ad‑ranking, trajectory‑based CTR stability, and task‑aware decoding for large language models.

CTR predictionComputer VisionE‑commerce

0 likes · 20 min read

Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)

Meituan Technology Team

Oct 31, 2024 · Artificial Intelligence

Selected Meituan Papers from CIKM 2024: Summaries of Eight Research Works

This article highlights eight Meituan research papers accepted at CIKM 2024—spanning self‑supervised sequential recommendation, rating‑consistent explanation generation, CTR prediction via recommendation pre‑training, cross‑domain interest transfer, multimodal vector retrieval, design‑aware poster layout, order‑fulfillment cycle‑time forecasting, and delivery‑scope substitution—offering insights from both internal and university collaborations.

AI researchCTR predictionCross‑Domain Recommendation

0 likes · 16 min read

Selected Meituan Papers from CIKM 2024: Summaries of Eight Research Works

iQIYI Technical Product Team

Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

AdvertisingCTR predictionDeep Learning

0 likes · 9 min read

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

Tencent Advertising Technology

Aug 27, 2024 · Artificial Intelligence

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction

This study investigates how adding an auxiliary ranking loss to click‑through‑rate (CTR) models not only improves ranking but also alleviates gradient‑vanishing for negative samples, thereby boosting the primary classification performance, especially under sparse positive‑feedback conditions.

AdvertisingCTR predictiongradient analysis

0 likes · 13 min read

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction

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 predictionRecommendation Systemscontrastive learning

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 LearningRecommendation Systems

0 likes · 21 min read

Multimodal Representations Boost Taobao Display Advertising CTR

Tencent Advertising Technology

Jul 24, 2024 · Artificial Intelligence

Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse

This paper investigates the embedding dimensional collapse problem that hinders scaling of recommendation models and proposes a Multi-Embedding paradigm that learns multiple embeddings per feature with independent expert networks, demonstrating consistent performance gains across major CTR benchmarks and real‑world ad systems.

CTR predictionDeep Learningartificial intelligence

0 likes · 10 min read

Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse

Alimama Tech

Jun 13, 2024 · Artificial Intelligence

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction

The article describes Alibaba's approach to distilling privileged features for CTR prediction using a calibration-compatible listwise distillation loss (CLID) that normalizes teacher and student outputs within sessions to align top‑ranking probabilities, improving both accuracy and ranking while preserving calibration.

AI in advertisingCTR predictionListwise distillation

0 likes · 14 min read

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction

Ele.me Technology

Mar 21, 2024 · Artificial Intelligence

How FIN Boosts CTR in Online Food Ordering: A Spatial‑Temporal Modeling Breakthrough

The paper introduces FIN (Fragment and Integrate Network), a novel spatial‑temporal model that extracts multiple sub‑sequences from ultra‑long user behavior logs, applies simplified and multi‑head attention, and fuses them with physically meaningful set operations, achieving up to 5.7% CTR lift and 7.3% RPM improvement in real‑world food‑delivery advertising.

AICTR predictionLong Sequence Modeling

0 likes · 23 min read

How FIN Boosts CTR in Online Food Ordering: A Spatial‑Temporal Modeling Breakthrough

DataFunSummit

Jan 12, 2024 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article explains the fundamentals of graph neural networks and graph representation learning, outlines how graphs enhance recommendation systems, and details OPPO's practical implementation of a hybrid dual‑tower and graph sub‑network model to improve recall and ranking performance.

CTR predictionDeep LearningOPPO

0 likes · 19 min read

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

Ele.me Technology

Aug 17, 2023 · Artificial Intelligence

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

BASM is a bottom‑up adaptive spatiotemporal model for online food ordering that uses hierarchical embedding, semantic transformation, and adaptive bias layers to dynamically modulate parameters according to time and location, thereby capturing multiple data distributions and achieving superior offline metrics and online A/B test performance.

CTR predictionRecommendation Systemsadaptive parameters

0 likes · 18 min read

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

Alimama Tech

Aug 9, 2023 · Artificial Intelligence

Highlights of Alibaba Mama Team Papers Accepted at CIKM 2023

Eight Alibaba Mama team papers accepted at CIKM 2023 present advances such as task‑specific bottom‑representation networks for recommendation, a unified GNN for multi‑scenario e‑commerce search, multi‑slot bid shading, consistency‑oriented pre‑ranking, bias‑mitigating CTR prediction, efficient progressive‑sampling self‑attention, delayed‑feedback conversion modeling, and hybrid contrastive multi‑scenario ad ranking.

AICTR predictionGraph Neural Network

0 likes · 13 min read

Highlights of Alibaba Mama Team Papers Accepted at CIKM 2023

Kuaishou Tech

Aug 8, 2023 · Artificial Intelligence

TWIN: Two-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction

This paper presents TWIN, a two-stage interest network that aligns the similarity metrics of coarse‑grained and fine‑grained modules to improve lifelong user behavior modeling for CTR prediction in large‑scale online recommendation systems.

Attention MechanismCTR predictionKuaishou

0 likes · 10 min read

TWIN: Two-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction

Alimama Tech

Jul 5, 2023 · Artificial Intelligence

Maria: Multi-Scenario Ranking with Adaptive Feature Learning

Maria is a multi‑scenario ranking framework that adaptively learns features across heterogeneous e‑commerce query types—visual search, similar‑product search, and interest search—by employing Feature Scaling, Feature Refinement, and Feature Correlation Modeling modules, achieving superior performance and reducing the seesaw effect on the Ali‑CCP and Alimama datasets.

CTR predictionE-commerce Searchadaptive feature learning

0 likes · 11 min read

Maria: Multi-Scenario Ranking with Adaptive Feature Learning

Bilibili Tech

Jun 27, 2023 · Artificial Intelligence

Design and Implementation of a Real-Time Advertising Feature Platform for CTR Prediction at Bilibili

To eliminate data fragmentation, feature inconsistencies, and multi‑language implementation challenges, Bilibili built a unified real‑time advertising feature platform that aligns offline, hourly, and online pipelines via a shared C++ library and JNI, boosting CTR prediction accuracy, cutting training costs, and increasing ad revenue by over 1 %.

AdvertisingCTR predictionDeep Learning

0 likes · 11 min read

Design and Implementation of a Real-Time Advertising Feature Platform for CTR Prediction at Bilibili

Alimama Tech

Jun 21, 2023 · Artificial Intelligence

Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction

The Joint Optimization of Ranking and Calibration (JRC) model introduces a two‑logit generative‑discriminative architecture that jointly minimizes LogLoss for calibration and a listwise ranking loss, delivering superior GAUC and CTR performance across Alibaba’s display‑ad system, especially for sparse long‑tail users, while remaining simple to train and deploy.

CTR predictionCalibrationHybrid Model

0 likes · 18 min read

Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction

Alimama Tech

May 10, 2023 · Artificial Intelligence

How AdaSparse Boosts Multi‑Scenario CTR Prediction with Adaptive Sparse Networks

AdaSparse introduces an adaptive sparse network that learns a dedicated sub‑network for each advertising scenario, balancing shared and specific knowledge while keeping computational cost low, and achieves +4.63% CTR and -3.82% CPC improvements in Alibaba’s external ad system, as validated on both public and massive production datasets.

AdvertisingCTR predictionDeep Learning

0 likes · 20 min read

How AdaSparse Boosts Multi‑Scenario CTR Prediction with Adaptive Sparse Networks

Alimama Tech

Dec 21, 2022 · Artificial Intelligence

Adaptive Parameter Generation Network for Click-Through Rate Prediction

Adaptive Parameter Generation Network (APG) dynamically creates sample‑specific model parameters for click‑through‑rate prediction using low‑rank factorization, parameter sharing, and over‑parameterization, achieving up to 0.2% AUC improvement, 3% CTR lift, and up to 96.6% storage reduction with faster inference.

CTR predictionDeep Learningadaptive parameter generation

0 likes · 14 min read

Adaptive Parameter Generation Network for Click-Through Rate Prediction

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

DataFunTalk

Oct 24, 2022 · Artificial Intelligence

Efficient Target Attention (ETA) for Long-Term User Behavior Modeling in Click‑Through Rate Prediction

Efficient Target Attention (ETA) introduces a low‑cost hash‑based attention operator that enables end‑to‑end modeling of ultra‑long user behavior sequences for CTR prediction, achieving significant online CTR, GMV, and QPS improvements in Alibaba’s Taobao feed recommendation system.

Attention MechanismCTR predictionHashing

0 likes · 20 min read

Efficient Target Attention (ETA) for Long-Term User Behavior Modeling in Click‑Through Rate Prediction

Alimama Tech

Oct 12, 2022 · Artificial Intelligence

Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

Decoupled Graph Neural Networks (DC‑GNN) improve large‑scale e-commerce ad recall by separating graph processing from CTR prediction, using multi‑task pretraining (edge prediction + contrastive learning), efficient deep linear aggregation, and a dual‑tower CTR model, achieving higher efficiency and performance on billions‑scale data.

CTR predictionDecoupled ArchitectureLarge-Scale Graph

0 likes · 15 min read

Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

Meituan Technology Team

Sep 8, 2022 · Artificial Intelligence

Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction

The article details Meituan Waimai's use of graph neural network techniques—feature‑graph crossing, subgraph expansion, and metapath‑based scene graphs—to model user‑restaurant interactions across location, time, and context, describing the engineering pipeline, online serving optimizations, and offline AUC improvements of up to 2.5 ‰ for high‑ and low‑frequency scenarios.

CTR predictionMeituan WaimaiRecommendation Systems

0 likes · 29 min read

Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction

NetEase Yanxuan Technology Product Team

Aug 15, 2022 · Artificial Intelligence

Evolution of the First-Focus Personalized Recommendation Model in E-commerce

The article details a step‑by‑step evolution of an e‑commerce platform’s top‑slot recommendation system, moving from a DCN‑mix single‑objective model through BST‑based dynamic features, position‑bias debiasing, multi‑task MMoE learning, and finally BST with target‑attention, each yielding measurable CTR, conversion, and user‑value gains.

CTR predictionmulti-task learningposition bias

0 likes · 22 min read

Evolution of the First-Focus Personalized Recommendation Model in E-commerce

DataFunSummit

Jul 25, 2022 · Artificial Intelligence

Intelligent Creative System at Hello: Business Background, Architecture, Implementation, and Reflections

This article presents Hello's Intelligent Creative project, detailing its business motivations, system architecture, algorithmic choices such as seq2seq, VAE, GAN, and pre‑trained models, the implementation of material libraries, tagging, recall strategies, a creative racing model, performance gains, and future challenges.

AICTR predictionad generation

0 likes · 16 min read

Intelligent Creative System at Hello: Business Background, Architecture, Implementation, and Reflections

JD Tech

Jul 21, 2022 · Artificial Intelligence

Improving JD Retail Recommendation Advertising Ranking with Variational Feature Learning, User Interest Network Optimization, and Global Collaborative Modeling

This article presents JD's comprehensive technical solution for boosting recommendation ad ranking by addressing cold‑start, shallow user interest extraction, and insufficient global data through a variational feature learning framework, enhanced user‑interest networks, and full‑domain collaborative modeling, achieving over 1% AUC gain and notable revenue growth.

CTR predictionDeep Learninge‑commerce

0 likes · 21 min read

Improving JD Retail Recommendation Advertising Ranking with Variational Feature Learning, User Interest Network Optimization, and Global Collaborative Modeling

JD Retail Technology

Jun 27, 2022 · Artificial Intelligence

Advances in JD E‑commerce Advertising CTR Prediction: Variational Feature Learning, User Interest Network Optimization, and Global User Collaborative Modeling

This article presents JD's end‑to‑end improvements for advertising click‑through‑rate prediction, addressing cold‑start, deep user‑interest mining, and full‑domain collaborative information through a variational feature learning framework, enhanced interest networks (PPNet+, NeNet, Weighted‑MMoE) and exposure‑sequence modeling, achieving over 1% cumulative AUC gain and publication in top conferences.

CTR predictione-commerce recommendationmulti-task learning

0 likes · 21 min read

Advances in JD E‑commerce Advertising CTR Prediction: Variational Feature Learning, User Interest Network Optimization, and Global User Collaborative Modeling

DataFunTalk

May 4, 2022 · Artificial Intelligence

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

This article reviews the evolution of recommendation models from early collaborative filtering to modern deep learning approaches, discusses core challenges such as CTR prediction, outlines user‑behavior and combination‑feature modeling techniques, introduces large‑embedding training and continuous‑feature embedding methods like AutoDis, and presents distributed training frameworks such as ScaleFreeCTR, concluding with future research directions.

CTR predictionDeep LearningEmbedding

0 likes · 21 min read

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

Mafengwo Technology

Mar 24, 2022 · Artificial Intelligence

How MaFengWo Reduces Position Bias in Its Recommendation Ranking System

This article explains how MaFengWo's recommendation ranking system tackles position bias by incorporating position features, using inverse propensity weighting, and adjusting click metrics, resulting in measurable improvements in click‑through rate, content exposure, and overall recommendation accuracy.

CTR predictioninverse propensity weightingposition bias

0 likes · 10 min read

How MaFengWo Reduces Position Bias in Its Recommendation Ranking System

Tencent Cloud Developer

Mar 15, 2022 · Artificial Intelligence

Comprehensive Overview of Ranking Models in Recommendation Systems

The article provides a thorough guide to ranking in recommendation systems, detailing the pipeline architecture, sample handling challenges, extensive feature engineering categories, the evolution from collaborative filtering to deep and attention‑based models, and key optimization trade‑offs between memorization, generalization, and efficient user‑interest modeling.

CTR predictionDeep LearningModel Optimization

0 likes · 19 min read

Comprehensive Overview of Ranking Models in Recommendation Systems

DataFunTalk

Mar 12, 2022 · Artificial Intelligence

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

This article presents a comprehensive overview of NetEase Cloud Music's advertising system, detailing its architecture, core challenges, CTR and CVR prediction models, feature engineering, model evolution from LR to deep learning, user vector modeling, and practical recommendations for improving ad performance.

AdvertisingCTR predictionDeep Learning

0 likes · 15 min read

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

DataFunTalk

Feb 24, 2022 · Artificial Intelligence

Sequence Optimization and Context-Aware CTR Re-Estimation for JD Advertising Ranking

The article presents JD's technical evolution for advertising ranking, covering recommendation ad sorting, context‑aware CTR re‑estimation, reinforcement‑learning‑based sequence optimization, and session‑level auction mechanisms, and includes a Q&A that highlights practical gains and implementation challenges.

AdvertisingCTR predictionContext-Aware

0 likes · 14 min read

Sequence Optimization and Context-Aware CTR Re-Estimation for JD Advertising Ranking

58 Tech

Feb 24, 2022 · Artificial Intelligence

Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com

This report details how the Search Recommendation team at 58.com upgraded their deep learning ranking model for recruitment by adding multi-valued and semantic vector features, integrating conversion sequences, employing feature‑crossing techniques, optimizing offline data pipelines, and planning future multi‑scene improvements to boost CTR and relevance.

AICTR predictionfeature engineering

0 likes · 18 min read

Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com

Shopee Tech Team

Feb 17, 2022 · Artificial Intelligence

From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot

The article details Shopee Chatbot’s end‑to‑end development of a dropdown recommendation feature, describing the retrieve‑then‑rank architecture with BM25 and vector recalls, multilingual pre‑training and distillation, DeepFM‑based ranking, experimental gains in CTR and conversion, deployment infrastructure, business impact, and future enhancements.

CTR predictionChatbotVector Retrieval

0 likes · 20 min read

From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot

DataFunTalk

Jan 19, 2022 · Artificial Intelligence

ZEUS: A Self‑Supervised Multi‑Scenario Query Ranking Model for E‑commerce Search

The article presents ZEUS, a self‑supervised multi‑scenario ranking model that leverages user‑initiated behavior pre‑training to break feedback loops and improve query recommendation efficiency across diverse e‑commerce search scenarios, achieving significant gains in CTR, CVR, and GMV.

CTR predictionmulti-scenario rankingquery recommendation

0 likes · 19 min read

ZEUS: A Self‑Supervised Multi‑Scenario Query Ranking Model for E‑commerce Search

IEG Growth Platform Technology Team

Dec 20, 2021 · Artificial Intelligence

Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems

This article presents a three‑part technical guide covering the fundamentals of computational advertising and real‑time bidding, detailed offline pCTR model training pipelines with feature engineering, calibration and model structure improvements, and advanced online learning techniques such as parameter freezing, sample replay and knowledge distillation, all aimed at boosting CTR performance and reducing bias in large‑scale ad platforms.

AdvertisingCTR predictionOnline Learning

0 likes · 37 min read

Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems

Meituan Technology Team

Oct 14, 2021 · Artificial Intelligence

Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising

Meituan's ad team uses deep learning to handle LBS distance constraints and long‑term periodic behavior, introducing DPIN for position/context bias, an ultra‑long sequence encoder with spatiotemporal activator, dynamic candidate generation, and memory‑augmented continual learning, boosting RPM 2‑20% and enabling sub‑millisecond inference.

AdvertisingCTR predictionDeep Learning

0 likes · 29 min read

Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising

58 Tech

Sep 24, 2021 · Artificial Intelligence

58.com AI Algorithm Competition: Award Ceremony, Top Teams, and Solution Sharing

The 58.com AI algorithm competition showcased over 210 teams competing to improve job recommendation click‑through and conversion rates, featured an award ceremony with speeches, highlighted the ten winning teams, and presented detailed solution shares—including tree models, feature‑engineering techniques, and deep‑learning approaches—while offering GPU resources on the WPAI platform for continued participation.

AI competitionCTR predictionModel Optimization

0 likes · 10 min read

58.com AI Algorithm Competition: Award Ceremony, Top Teams, and Solution Sharing

DataFunSummit

Aug 28, 2021 · Artificial Intelligence

Evolution of Alibaba’s Advertising Prediction Models: From Linear Regression to Deep Interest Evolution Networks

This article reviews the characteristics of e‑commerce personalized prediction, traces Alibaba’s advertising CTR model evolution from large‑scale logistic regression through deep learning architectures such as DIN and CrossMedia, and discusses future research directions like representation learning and white‑box modeling.

CTR predictionDeep LearningE‑commerce

0 likes · 13 min read

Evolution of Alibaba’s Advertising Prediction Models: From Linear Regression to Deep Interest Evolution Networks

DataFunTalk

Aug 27, 2021 · Artificial Intelligence

Hybrid Bandit and Visual-aware Ranking Models for Advertising Creative Selection and Dynamic Optimization

The article presents a hybrid bandit framework combined with a visual‑aware ranking model to efficiently select and dynamically optimize advertising creatives, addressing cold‑start challenges, element‑level personalization, and production‑parameter search, and validates the approach with extensive offline and online experiments.

Bandit AlgorithmsCTR predictioncreative optimization

0 likes · 15 min read

Hybrid Bandit and Visual-aware Ranking Models for Advertising Creative Selection and Dynamic Optimization

Alimama Tech

Aug 18, 2021 · Artificial Intelligence

Overview of Recent Alibaba Mama Research Papers on AI and Large‑Scale Advertising Systems

The article surveys six Alibaba Mama papers accepted at CIKM 2021, presenting novel AI methods—including a heterogeneous graph neural network for keyword matching, a star‑topology multi‑domain CTR model, a compact hash embedding technique, adaptive masked twins layers, automated hierarchical conversion prediction, and a scalable multi‑view ad retrieval system—each demonstrating substantial online performance improvements and large‑scale deployment.

AIAdvertisingCTR prediction

0 likes · 11 min read

Overview of Recent Alibaba Mama Research Papers on AI and Large‑Scale Advertising Systems

Baidu Geek Talk

Aug 16, 2021 · Artificial Intelligence

End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems

The article describes Baidu’s end-to-end consistency testing framework for advertising click-through-rate models, which uses a five-stream verification pipeline and six implementation phases to compare Q-values across feature extraction, table conversions, and DNN computation, enabling precise detection and localization of data and model inconsistencies in production.

BaiduCTR predictionMachine learning testing

0 likes · 17 min read

End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems

DataFunTalk

Aug 10, 2021 · Artificial Intelligence

A Comprehensive Review of Industrial-Scale Deep Learning for Click-Through Rate Prediction in Online Advertising

This article provides an extensive retrospective and forward‑looking analysis of the evolution of click‑through‑rate prediction technologies in online advertising, covering shallow‑learning era challenges, the rise of industrial‑scale deep learning, system‑level innovations such as recall, coarse‑ranking, fine‑ranking, bidding, and the emerging co‑design of algorithms, compute, and architecture.

Algorithmic OptimizationCTR predictionCompute Efficiency

0 likes · 65 min read

A Comprehensive Review of Industrial-Scale Deep Learning for Click-Through Rate Prediction in Online Advertising

DataFunSummit

Aug 7, 2021 · Artificial Intelligence

Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

This article describes how Alibaba's advertising team tackled the challenges of modeling long‑term user interests for CTR prediction by co‑designing incremental computation services, introducing memory‑network‑based models (MIMN and HPMN), and achieving significant offline and online performance gains.

CTR predictionLong-Term InterestRecommendation Systems

0 likes · 17 min read

Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

DataFunTalk

Jun 14, 2021 · Artificial Intelligence

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

This article describes how Alibaba's search advertising team transformed trillion‑parameter CTR models into lightweight, high‑precision systems by compressing embedding layers through feature‑space reduction, dimension quantization, and multi‑hash techniques, while also introducing graph‑based pre‑training and dropout‑driven feature selection to maintain accuracy.

CTR predictionembedding reductionfeature selection

0 likes · 15 min read

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

Meituan Technology Team

Jun 10, 2021 · Artificial Intelligence

Deep Position-wise Interaction Network for CTR Prediction

The Meituan team introduces DPIN, a three‑module deep network that jointly models ads and their positions to mitigate position bias in CTR prediction, achieving up to 2.98% AUC improvement, 2.25% higher CTR and 2.15% RPM gains while keeping latency modest, and is applicable to broader ranking tasks.

AdvertisingCTR predictionDPIN

0 likes · 24 min read

Deep Position-wise Interaction Network for CTR Prediction

DataFunSummit

Mar 11, 2021 · Artificial Intelligence

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

This article presents the Search‑Based Interest Model (SIM), a two‑stage retrieval framework that indexes a user's entire behavior history to enable long‑term interest modeling for click‑through‑rate prediction, demonstrating practical deployment and improved recommendation of long‑term interests in e‑commerce.

AICTR predictionLong-Term Interest

0 likes · 16 min read

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

DataFunSummit

Feb 2, 2021 · Artificial Intelligence

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

This article systematically reviews the evolution of click‑through‑rate (CTR) prediction models—from early distributed linear models like logistic regression, through automated feature engineering with GBDT+LR, various factorization‑machine variants, embedding‑MLP shallow modifications, dual‑tower combinations, and advanced explicit feature‑cross networks—highlighting each model’s structure, advantages, limitations, and comparative insights.

CTR predictionclick-through ratefactorization machines

0 likes · 28 min read

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

DataFunTalk

Jan 27, 2021 · Artificial Intelligence

CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction

This article details the development and deployment of CAN (Co‑Action Net), a novel click‑through‑rate prediction model that captures item‑item co‑action via attention‑based slot embeddings, offering superior performance to Cartesian‑product methods while reducing parameter and serving costs.

CTR predictionCo-Action Netfeature interaction

0 likes · 14 min read

CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction

58 Tech

Nov 11, 2020 · Artificial Intelligence

Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation

This article details how 58.com leverages deep learning models such as DNN, Wide&Deep, DeepFM, DIN and DIEN, combined with extensive user‑behavior feature engineering, offline vectorization, and online TensorFlow‑Serving pipelines to improve home‑page recommendation click‑through rates and overall platform efficiency.

A/B testingAttention MechanismCTR prediction

0 likes · 25 min read

Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation

JD Retail Technology

Oct 10, 2020 · Artificial Intelligence

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

This article introduces a Kalman Filtering Attention (KFAtt) framework that enhances click‑through‑rate (CTR) prediction by modeling user behavior with a Kalman‑filter‑based attention mechanism and a frequency‑capped variant, addressing new‑interest coverage and frequency bias in e‑commerce scenarios.

Attention MechanismCTR predictionKalman Filter

0 likes · 11 min read

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

DataFunTalk

Sep 29, 2020 · Artificial Intelligence

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

This article introduces the Deep Sparse Network (NON), a new deep neural architecture for recommendation systems that combines field‑wise networks, across‑field interaction networks, and an operation‑fusion network, and demonstrates its superior performance through extensive experiments and ablation studies.

CTR predictionDeep Learningfeature interaction

0 likes · 14 min read

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

DataFunTalk

Sep 18, 2020 · Artificial Intelligence

MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction

This article reviews the MiNet model, which leverages cross‑domain information by modeling long‑term, source‑domain short‑term, and target‑domain short‑term user interests with hierarchical attention and an auxiliary task to improve CTR prediction and alleviate cold‑start issues.

Attention MechanismCTR predictionMiNet

0 likes · 12 min read

MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction

Beike Product & Technology

Sep 4, 2020 · Artificial Intelligence

Wide & Deep Model for Real‑Estate Purchase Intent Prediction

This article presents a comprehensive study of the Wide & Deep architecture applied to user purchase‑intent quantification in the real‑estate domain, detailing feature engineering, model design, training procedures, experimental results, and extensions with GRU‑based sequential modeling to improve accuracy.

CTR predictionDeep LearningReal Estate

0 likes · 15 min read

Wide & Deep Model for Real‑Estate Purchase Intent Prediction

Tencent Cloud Developer

Sep 3, 2020 · Artificial Intelligence

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

The paper presents an optimized CTR prediction model for Tencent’s App Store that merges multi‑behavior shared embeddings, long‑term DeepWalk graph embeddings, BERT‑derived app description vectors, and attention‑based fusion, reducing parameters while improving bias, AUC, and recommendation performance for sparse, long‑tail data.

BERTCTR predictionDeepWalk

0 likes · 9 min read

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

DataFunTalk

Sep 3, 2020 · Artificial Intelligence

Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com

This article describes how 58.com applied deep‑learning techniques—including feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN and multi‑task learning—and system‑level optimizations to improve CTR/CPM performance in its large‑scale commercial ranking platform.

CTR predictionDeep LearningSystem optimization

0 likes · 38 min read

Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com

DataFunTalk

Sep 2, 2020 · Artificial Intelligence

CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising

This article presents CSCNN, a category‑specific convolutional neural network that integrates visual priors into click‑through‑rate (CTR) models for JD.com’s e‑commerce advertising, detailing its motivation, architecture, engineering optimizations, offline and online training strategies, and empirical performance gains on both public and industrial datasets.

CTR predictionDeep Learningcategory-specific CNN

0 likes · 19 min read

CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising

58 Tech

Aug 31, 2020 · Artificial Intelligence

Deep Learning Practices for Commercial CTR Prediction at 58.com

This article details the end‑to‑end deep‑learning workflow for click‑through‑rate (CTR) prediction in 58.com’s commercial ranking system, covering system architecture, feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN, and engineering optimizations that together yielded significant CPM and CVR improvements.

AdvertisingCTR predictionDeep Learning

0 likes · 38 min read

Deep Learning Practices for Commercial CTR Prediction at 58.com

DataFunTalk

Aug 29, 2020 · Artificial Intelligence

User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

This article presents Alibaba's comprehensive approach to user modeling for search CTR/CVR ranking, detailing the abstraction of user information, multi‑scale behavior processing, enhanced transformer‑based model structures, client‑side click and exposure modeling, and experimental results showing significant AUC improvements.

AlibabaAttention MechanismCTR prediction

0 likes · 18 min read

User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

DataFunTalk

Jul 21, 2020 · Artificial Intelligence

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

This article details the end‑to‑end technical architecture of WeChat's "Look" personalized recommendation service, covering data collection, recall, multi‑stage ranking, various CTR and multi‑objective models, reinforcement‑learning based mixing, diversity optimization, and the engineering hurdles overcome to deploy these solutions at massive scale.

CTR predictionDeep LearningWeChat AI

0 likes · 17 min read

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

JD Retail Technology

Jul 15, 2020 · Artificial Intelligence

Category-Specific CNN for Visual-Aware Click‑Through Rate Prediction at JD.com

The paper introduces a Category‑Specific Convolutional Neural Network (CSCNN) that jointly leverages product category information and visual features of e‑commerce images to improve click‑through‑rate (CTR) prediction, detailing its architecture, training strategy, large‑scale experiments, and significant performance gains in JD.com’s advertising system.

AdvertisingCTR predictioncategory-specific CNN

0 likes · 13 min read

Category-Specific CNN for Visual-Aware Click‑Through Rate Prediction at JD.com

DataFunTalk

May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictionDeep LearningRecommendation Systems

0 likes · 10 min read

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

DataFunTalk

May 11, 2020 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

This article reviews recent innovations in CTR prediction for an intelligent marketing platform, covering deep spatio‑temporal networks, deep memory networks, and a feature‑expression‑assisted learning framework, with system architecture details, experimental results, and references to KDD and IJCAI papers.

AdvertisingCTR predictionDeep Learning

0 likes · 15 min read

Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

DataFunTalk

Apr 21, 2020 · Artificial Intelligence

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

This article surveys the application of attention mechanisms in deep learning recommendation systems, reviewing models such as AFM, DIN, DIEN, DSIN, Behavior Sequence Transformer, Deep Spatio‑Temporal Networks, and ATRank, and discusses their architectures, attention types, advantages, and limitations.

CTR predictionDeep LearningRecommendation Systems

0 likes · 10 min read

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

DataFunTalk

Apr 13, 2020 · Artificial Intelligence

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

This article presents the design, challenges, and experimental evaluation of DSTN (with pooling, self‑attention, and interactive‑attention variants) and MA‑DNN models for CTR prediction, highlighting how temporal and contextual ad information improves accuracy and yields significant online gains in large‑scale advertising systems.

AdvertisingCTR predictionDeep Learning

0 likes · 16 min read

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

DataFunTalk

Apr 8, 2020 · Artificial Intelligence

Dynamic Creative Optimization and DeepMCP Feature Learning for CTR Prediction

This talk presents a dynamic creative optimization framework that combines style‑material selection with DSA modeling to address the combinatorial explosion in CTR prediction, and introduces DeepMCP, an auxiliary network that improves feature embeddings through user‑ad and ad‑ad relationships, achieving superior performance in large‑scale advertising systems.

CTR predictionDeepMCPDynamic creative optimization

0 likes · 21 min read

Dynamic Creative Optimization and DeepMCP Feature Learning for CTR Prediction

Alibaba Cloud Developer

Mar 13, 2020 · Artificial Intelligence

How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations

The article presents the Deep Match to Rank (DMR) model, which integrates collaborative‑filtering inspired user‑to‑item relevance modeling into the ranking stage of recommendation systems, achieving significant offline and online improvements in click‑through rate and revenue metrics for e‑commerce platforms.

CTR predictionDeep Learninge‑commerce

0 likes · 11 min read

How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations

DataFunTalk

Mar 12, 2020 · Artificial Intelligence

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

This article describes the end‑to‑end recommendation pipeline of the Province Money Fast Report app, covering business background, data collection, model training and evaluation, the evolution from FM to DeepFM, DIN, DCN, xDeepFM, ESMM and custom networks, as well as serving strategies and practical lessons learned.

CTR predictionDeep LearningModel Serving

0 likes · 28 min read

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

Qunar Tech Salon

Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR predictionDeep LearningRecommendation Systems

0 likes · 12 min read

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

Qunar Tech Salon

Feb 27, 2020 · Artificial Intelligence

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

This article describes iQIYI’s dual‑DNN ranking architecture that combines a high‑capacity teacher network with a lightweight student network via online knowledge distillation, addressing the trade‑off between model effectiveness and inference efficiency in large‑scale recommendation systems.

CTR predictionOnline LearningRanking Models

0 likes · 12 min read

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

iQIYI Technical Product Team

Feb 21, 2020 · Artificial Intelligence

Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems

iQIYI’s dual‑DNN ranking model uses an online teacher‑student knowledge‑distillation framework where a complex teacher DNN shares representations with a lightweight student DNN, enabling end‑to‑end training, large‑scale feature crossing, and substantially higher recommendation accuracy while cutting inference latency and model size.

CTR predictionOnline Learningdual DNN

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Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems

DataFunTalk

Feb 5, 2020 · Artificial Intelligence

Deep Match to Rank Model for Personalized Click-Through Rate Prediction

This article presents the Deep Match to Rank (DMR) model, which integrates matching and ranking stages in recommendation systems by jointly learning user‑to‑item and item‑to‑item representations with attention mechanisms, achieving significant CTR and DPV improvements in both offline experiments and large‑scale online deployments.

CTR prediction

0 likes · 11 min read

Deep Match to Rank Model for Personalized Click-Through Rate Prediction

Alibaba Cloud Developer

Jan 7, 2020 · Artificial Intelligence

How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention

This article details Alibaba’s Taobao search CTR/CVR user modeling approach, covering background, model architecture with self‑attention and attention pooling, handling short‑term, long‑term, and on‑device behavior sequences, experimental results showing AUC improvements, and future directions.

CTR predictionSelf-Attentionbehavior sequence

0 likes · 20 min read

How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention

Alibaba Cloud Developer

Dec 12, 2019 · Artificial Intelligence

How Multi‑Layer Multi‑Frequency Streaming Training Boosts Real‑Time CTR/CVR Prediction

This article details a novel Multi‑Layer Multi‑Frequency streaming training approach that enables minute‑level real‑time updates of massive CTR/CVR models by partitioning weights into freezing embeddings, changing embeddings, and changing weights, demonstrating significant offline and online AUC gains, especially during high‑traffic events like Double 11.

CTR predictione‑commercemachine learning

0 likes · 18 min read

How Multi‑Layer Multi‑Frequency Streaming Training Boosts Real‑Time CTR/CVR Prediction

DataFunTalk

Nov 4, 2019 · Artificial Intelligence

Standardizing Model Training and Feature Processing in Recommendation Systems

This article describes a standardized workflow for feature collection, configuration, processing, and model training/prediction in large‑scale recommendation systems, using CSV‑based definitions and code generation to ensure consistency between offline training and online serving while reducing manual coding effort.

CTR predictionModel Trainingfeature engineering

0 likes · 14 min read

Standardizing Model Training and Feature Processing in Recommendation Systems

DataFunTalk

Sep 12, 2019 · Artificial Intelligence

Exploring Personalized Recommendation at Kuaikan Comics: Business, Algorithms, and System Architecture

This article details Kuaikan Comics' personalized recommendation pipeline, covering business context, diverse content formats, technical challenges, content‑based and collaborative‑filtering methods, ranking models, system architecture, A/B testing, and future directions for improving recommendation quality.

A/B testingCTR predictionSystem Architecture

0 likes · 14 min read

Exploring Personalized Recommendation at Kuaikan Comics: Business, Algorithms, and System Architecture

Snowball Engineer Team

Sep 4, 2019 · Artificial Intelligence

Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking

This article explores the evolution of recommendation algorithms at Xueqiu, highlighting the limitations of traditional point-wise click-through rate prediction models and detailing the ongoing transition to a pair-wise TF-Ranking framework designed to mitigate user and content biases while significantly enhancing overall recommendation accuracy and user experience.

Algorithm OptimizationCTR predictionPair-Wise Learning

0 likes · 5 min read

Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking

Alibaba Cloud Developer

Aug 14, 2019 · Artificial Intelligence

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

This article presents a co-designed algorithm‑system solution—MIMN and an independent UIC module—that enables ultra‑long user behavior modeling for click‑through rate prediction, delivering significant offline AUC gains and online CTR/RPM improvements in Alibaba's display advertising platform.

CTR predictionDeep LearningRecommendation Systems

0 likes · 12 min read

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

Alibaba Cloud Developer

Jun 25, 2019 · Artificial Intelligence

How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue

This article explains how Alibaba's App recommendation pipeline integrates marketing scenario cards using weak personalization and machine‑learning models, detailing the metrics, feature engineering, recall and ranking strategies that together raise exposure revenue and click‑through performance.

AlibabaCTR predictioncard insertion

0 likes · 8 min read

How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue

DataFunTalk

May 20, 2019 · Artificial Intelligence

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

The article reviews the characteristics of e‑commerce personalized prediction, outlines Alibaba's model iteration from large‑scale linear regression to deep learning architectures such as DIN, CrossMedia, and Deep Interest Evolution, and discusses future directions like disentangled representation and white‑box modeling.

Attention MechanismCTR predictionRecommendation Systems

0 likes · 11 min read

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

Alibaba Cloud Developer

Dec 21, 2018 · Artificial Intelligence

X-DeepLearning: Alibaba’s Open‑Source Framework for Large‑Scale Sparse Deep Learning

Alibaba's X‑DeepLearning (XDL) is an open‑source deep‑learning framework optimized for high‑dimensional sparse data, offering industrial‑grade distributed training, built‑in CTR/recommendation algorithms, structured compression, and online learning capabilities, with benchmark results demonstrating superior scalability and performance.

CTR predictionDeep LearningDistributed Training

0 likes · 18 min read

X-DeepLearning: Alibaba’s Open‑Source Framework for Large‑Scale Sparse Deep Learning

DataFunTalk

Oct 26, 2018 · Artificial Intelligence

Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction

The article explains how large‑scale machine learning and AutoML are applied to search advertising click‑through‑rate (CTR) prediction, covering problem definition, feature generation, model training, optimization methods, distributed systems, and recent advances in AutoML with practical case studies.

AutoMLCTR predictionLarge-scale ML

0 likes · 15 min read

Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction

Alibaba Cloud Developer

Oct 9, 2018 · Artificial Intelligence

How Rocket Launching Boosts Online CTR Prediction Without Slowing Inference

Rocket Launching introduces a novel co‑training framework that jointly trains a lightweight network and a more powerful booster network, sharing parameters and using gradient‑blocking and hint loss to improve click‑through‑rate prediction accuracy while keeping online inference latency unchanged, validated on public datasets and Alibaba’s ad system.

CTR predictionco-traininggradient block

0 likes · 13 min read

How Rocket Launching Boosts Online CTR Prediction Without Slowing Inference

Alibaba Cloud Developer

Sep 11, 2018 · Artificial Intelligence

Rocket Launching: Boosting Real-Time CTR Prediction Without Extra Latency

Online click‑through‑rate (CTR) prediction demands millisecond‑level response times, yet deep models are too slow; this paper introduces a “Rocket Launching” framework that jointly trains a lightweight net and a powerful booster net, sharing parameters and using gradient‑blocking and hint loss to improve accuracy without increasing inference latency.

CTR predictionDeep Learningco-training

0 likes · 13 min read

Rocket Launching: Boosting Real-Time CTR Prediction Without Extra Latency

Qizhuo Club

Sep 11, 2018 · Artificial Intelligence

How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System

This article details the design, architecture, and key components of 360 Mobile Assistant's recommendation system, covering business scenarios, data warehouse and computing layers, feature engineering, model selection, and online deployment strategies to improve app discovery and user engagement.

CTR predictionData Warehousefeature engineering

0 likes · 19 min read

How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System

58 Tech

Sep 7, 2018 · Artificial Intelligence

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

The article details how 58.com’s Cupid push control system leverages machine‑learning models, especially XGBoost‑based CTR prediction, to prioritize and filter billions of daily push notifications, improving click‑through rates, reducing user annoyance, and providing a scalable, data‑driven architecture for diverse business services.

AB testingCTR predictionSystem Architecture

0 likes · 13 min read

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

Alibaba Cloud Developer

Aug 28, 2018 · Artificial Intelligence

Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization

This article introduces Wide&Deep, PNN, DeepFM, and a novel Wide‑ResNet model for Alibaba’s 1688 “Guess You Like” recommendation, explains the underlying feature services and real‑time scoring pipeline, presents offline experiments showing AUC gains with batch normalization, and shares practical tuning insights.

AlibabaCTR predictionDeep Learning

0 likes · 13 min read

Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization

Alibaba Cloud Developer

Jul 2, 2018 · Artificial Intelligence

How Visualizing Deep CTR Models Turns Black Boxes into Insightful Tools

This article presents DeepInsight, an industrial‑grade visual analytics platform that reveals the inner workings of large‑scale deep learning CTR prediction models, demonstrating how visualization can assess generalization, feature influence, and hidden‑layer representations to improve advertising performance.

CTR predictionDeepInsightadvertising AI

0 likes · 10 min read

How Visualizing Deep CTR Models Turns Black Boxes into Insightful Tools

Alibaba Cloud Developer

Apr 16, 2018 · Artificial Intelligence

How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks

This article recounts Alibaba‑Mama researcher Jing Shi’s presentation on the evolution of deep learning for click‑through‑rate (CTR) estimation, covering the shift from handcrafted features and linear models to piecewise linear MLR, end‑to‑end neural networks, multi‑interest user modeling, and large‑scale distributed training challenges.

AdvertisingCTR predictionDeep Learning

0 likes · 16 min read

How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks

Tongcheng Travel Technology Center

Jan 18, 2018 · Artificial Intelligence

Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation

This article describes a tourism recommendation system that addresses data sparsity, seasonality, and geographic variations by using an offline‑online architecture, GBDT+LR CTR prediction, exponential decay scoring, and extensive feature engineering, achieving a 1.6% conversion‑rate increase and high accuracy and recall.

CTR predictionGBDTTourism

0 likes · 14 min read

Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation

Meituan Technology Team

Jul 28, 2017 · Artificial Intelligence

Deep Learning Applications in Meituan‑Dianping Recommendation System

The paper describes Meituan‑Dianping’s two‑stage recommendation pipeline—recall and ranking—and how a Wide & Deep neural architecture, enriched with extensive user, item, and context features and trained with Adam and cross‑entropy loss, significantly boosts CTR and recommendation novelty, with future plans to add RNNs and reinforcement learning.

CTR predictionWide&Deepoptimization

0 likes · 21 min read

Deep Learning Applications in Meituan‑Dianping Recommendation System

Alibaba Cloud Developer

Jun 15, 2017 · Artificial Intelligence

How Alibaba’s Mixed Logistic Regression Revolutionizes CTR Prediction

This article explains the technical background of click‑through‑rate (CTR) prediction, critiques traditional linear models, introduces Alibaba’s Mixed Logistic Regression (MLR) algorithm with its advanced features and large‑scale distributed implementation, and reviews its successful deployment and remaining challenges in advertising systems.

AdvertisingCTR predictionMLR

0 likes · 13 min read

How Alibaba’s Mixed Logistic Regression Revolutionizes CTR Prediction

Tencent Advertising Technology

May 19, 2017 · Artificial Intelligence

Tencent Social Ads Algorithm Competition: Expert Q&A FAQ and Technical Insights

The expert live Q&A session for Tencent's Social Ads algorithm competition was recorded and the full FAQ, covering data labeling, feature handling, model selection, imbalance mitigation, and practical engineering tips, is presented here for participants and researchers alike.

AdvertisingCTR predictionXGBoost

0 likes · 14 min read