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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 ModelsSearch Advertising

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 generatione-commerce

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 predictioncontrastive learningdeep 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.

Artificial IntelligenceCTR predictionLarge Language Models

0 likes · 20 min read

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

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.

CTR predictionadvertisingdeep 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.

CTR predictionadvertisinggradient 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 predictionPretrainingRecommendation systems

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 predictionRecommendation systemscontrastive learning

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.

Artificial IntelligenceCTR predictiondeep learning

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

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 predictionGraph Neural NetworksOPPO

0 likes · 19 min read

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

JD Retail Technology

Nov 23, 2023 · Artificial Intelligence

Recent Advances in Advertising Recommendation Algorithms and Their Applications

This article reviews recent progress in advertising recommendation technologies, covering deep learning‑based ranking, sequence modeling, self‑supervised learning, online and reinforcement learning, multimodal recommendation, and fairness, and details four key breakthroughs—data‑driven incremental learning, dynamic group parameter modeling, bilateral interactive graph convolution, and a relation‑aware diffusion model for poster layout generation, along with experimental results and future challenges.

CTR predictionGraph Neural Networksadvertising recommendation

0 likes · 25 min read

Recent Advances in Advertising Recommendation Algorithms and Their Applications

DataFunSummit

Nov 5, 2023 · Artificial Intelligence

Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach

This article presents a memory‑driven architecture (HCNet and MemoNet) that equips recommendation models with scaling‑law characteristics by storing and retrieving arbitrary feature‑combination embeddings, evaluates multi‑hash codebooks, memory‑restoring strategies, key‑feature selection, and demonstrates significant offline and online performance gains.

CTR predictionLarge Language ModelsRecommendation systems

0 likes · 15 min read

Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach

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 predictionadvertising

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.

CTR predictionKuaishouTWIN

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 predictionadaptive feature learninge-commerce search

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 %.

CTR predictionFlinkadvertising

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 predictionHybrid ModelRanking

0 likes · 18 min read

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

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 predictionadaptive parameter generationdeep learning

0 likes · 14 min read

Adaptive Parameter Generation Network for Click-Through Rate Prediction