Search Advertising

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 predictionSearch Advertisinglarge language models

0 likes · 25 min read

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

Tencent Advertising Technology

Jan 9, 2025 · Artificial Intelligence

Applying Large Language Models to Search Advertising: End‑to‑End Generative Recall and System Optimizations

This report details how large language models (LLMs) were integrated into Tencent's search advertising pipeline—from early extraction‑distillation experiments in 2023 to a 2024 end‑to‑end generative recall architecture—showing significant improvements in relevance, diversity, and revenue through knowledge injection, supervised fine‑tuning, constrained beam‑search decoding, and high‑performance inference services.

AIKnowledge InjectionLLM

0 likes · 11 min read

Applying Large Language Models to Search Advertising: End‑to‑End Generative Recall and System Optimizations

Xiaohongshu Tech REDtech

Aug 1, 2024 · Artificial Intelligence

Xiaohongshu Search Advertising Recall: Practices, Metrics, and Large‑Model Integration

Xiaohongshu’s search advertising recall system evolves from keyword bidding to BERT‑based vector retrieval and LLM‑enhanced query rewriting, using dual semantic and efficiency models, water‑level metrics, and GPU‑accelerated engineering to achieve 80 % click coverage, 60 % conversion coverage and a 5 % CPM lift.

Artificial IntelligenceSearch Advertisingefficiency optimization

0 likes · 33 min read

Xiaohongshu Search Advertising Recall: Practices, Metrics, and Large‑Model Integration

DataFunSummit

Nov 3, 2023 · Artificial Intelligence

Applying Large Language Models to Search Advertising Satisfaction: From DNN to ERNIE and Prompt Learning

This article details Baidu Fengchao's practical use of large language models to improve search advertising satisfaction, covering search ad relevance, the transition from DNN to ERNIE, prompt-based industry isolation, AIGC applications, and a Q&A on model architecture and optimization.

AIERNIESearch Advertising

0 likes · 22 min read

Applying Large Language Models to Search Advertising Satisfaction: From DNN to ERNIE and Prompt Learning

DataFunTalk

Aug 13, 2023 · Artificial Intelligence

Applying Large Language Models to Search Advertising Satisfaction: From DNN to ERNIE and Prompt Learning

The article details how Baidu's Fengchao team leverages large language models, including a transition from DNN embeddings to ERNIE, introduces multi‑level tokenization and discrete core‑word inputs, and applies prompt learning and AIGC techniques to improve search advertising satisfaction and industry‑specific relevance modeling.

AIGCBaiduSearch Advertising

0 likes · 22 min read

Alimama Tech

Dec 21, 2022 · Artificial Intelligence

GBA: Global Batch Gradients Aggregation for Search Advertising Training

GBA (Global Batch Gradients Aggregation) introduces a training mode that seamlessly switches between synchronous and asynchronous learning for search‑advertising models by keeping a constant global batch size, using token‑controlled gradient aggregation and staleness management to retain synchronous‑level accuracy while preserving asynchronous efficiency and eliminating manual hyperparameter tuning.

AlibabaGBASearch Advertising

0 likes · 15 min read

GBA: Global Batch Gradients Aggregation for Search Advertising Training

Alimama Tech

Nov 16, 2022 · Artificial Intelligence

STARDOM: Semantic-Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction

STARDOM is an end‑to‑end deep hierarchical forecasting model that jointly learns hierarchical constraints, query semantics via pretrained BERT, and a calibration matrix within an encoder‑decoder architecture, using a distilled reconciliation loss and hierarchical sampling to accurately predict large‑scale search traffic and outperform state‑of‑the‑art baselines.

Search Advertisingdeep learninghierarchical modeling

0 likes · 22 min read

STARDOM: Semantic-Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction

DataFunSummit

Sep 13, 2022 · Artificial Intelligence

Elegant Integration of Ads in Search: An Analysis of Baidu's Mobius Approach

This article examines how search advertising can be seamlessly blended with user queries by balancing relevance and revenue, reviewing the evolution from portal indexing to recommendation systems, and detailing Baidu's Mobius framework that jointly optimizes relevance, CTR, and eCPM in a unified pipeline.

MobiusSearch Advertisingad ranking

0 likes · 24 min read

Elegant Integration of Ads in Search: An Analysis of Baidu's Mobius Approach

DataFunTalk

Aug 30, 2022 · Artificial Intelligence

Feature Engineering for Recommendation and Search Advertising

This article explains why meticulous feature engineering remains crucial in recommendation and search advertising, outlines what constitutes good features, describes common transformation techniques such as scaling, binning, and encoding, and provides practical examples and Q&A for practitioners.

AIFeature EngineeringRecommendation systems

0 likes · 18 min read

Feature Engineering for Recommendation and Search Advertising

Alimama Tech

Jun 22, 2022 · Artificial Intelligence

Graph Deep Learning: Methods, Frameworks, and Industrial Applications

Graph deep learning, extending deep models to irregular graph data via spatial and spectral GNNs such as GCN, GAT, and GraphSAGE, has matured into frameworks like Alibaba’s open‑source Euler, which scales to billions of nodes, powers a heterogeneous query‑item‑ad graph for search advertising, and demonstrably boosts click‑through rates by over 1.5%.

Euler frameworkGraph Neural NetworksSearch Advertising

0 likes · 17 min read

Graph Deep Learning: Methods, Frameworks, and Industrial Applications

Alimama Tech

Mar 23, 2022 · Artificial Intelligence

Advancements in Keyword Recall for Search Advertising: From Binary Retrieval to Hierarchical Bidding Graph

The paper reports a year‑long evolution of Alibaba’s search‑advertising keyword recall, replacing the traditional two‑stage rewrite‑and‑score pipeline with a low‑storage binary retrieval model and then a joint recall framework built on a hierarchical bidding graph, delivering near‑full‑precision recall, 16× memory savings, and quota‑free global ranking.

AISearch Advertisingbinary retrieval

0 likes · 22 min read

Advancements in Keyword Recall for Search Advertising: From Binary Retrieval to Hierarchical Bidding Graph

Alimama Tech

Dec 22, 2021 · Artificial Intelligence

HetMatch: Heterogeneous Graph Neural Network for Keyword Recommendation in Search Advertising

HetMatch is a heterogeneous graph neural network for keyword recommendation in search advertising that tackles cold‑start and large‑scale challenges by hierarchically fusing node and subgraph features, denoising graph convolutions, applying self‑attention, twin matching, and multi‑view learning, delivering notable recall gains and online performance improvements for Alibaba’s advertising tools.

Cold StartRecommendation systemsSearch Advertising

0 likes · 14 min read

HetMatch: Heterogeneous Graph Neural Network for Keyword Recommendation in Search Advertising

Alimama Tech

May 27, 2021 · Artificial Intelligence

Advances in Click‑Through Rate (CTR) Modeling: Overview of Recent SIGIR Papers and Optimization Paths

The article reviews recent Alibaba Mama advances in click‑through‑rate modeling, classifying optimizations across the three‑layer CTR architecture and highlighting three SIGIR papers—GIN’s graph‑based user intent modeling, PCF’s pre‑trained GNN for explicit cross‑feature semantics, and FSCD’s compute‑factor‑guided automatic feature selection—each boosting prediction accuracy and system efficiency.

CTR predictionGraph Neural NetworksSearch Advertising

0 likes · 12 min read

Advances in Click‑Through Rate (CTR) Modeling: Overview of Recent SIGIR Papers and Optimization Paths

Alimama Tech

May 27, 2021 · Artificial Intelligence

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre‑Ranking: A Learnable Feature‑Selection‑Based Approach

The authors introduce an interaction‑focused pre‑ranking model combined with a learnable, complexity‑aware feature‑selection technique (FSCD) that selects a compact feature set, enabling Alibaba’s search advertising system to boost offline AUC from 0.695 to 0.737, raise recall to 95 %, improve CTR and RPM, yet retain CPU usage and latency comparable to traditional vector‑dot models.

Search Advertisingeffectivenessefficiency

0 likes · 15 min read

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre‑Ranking: A Learnable Feature‑Selection‑Based Approach

DataFunTalk

Sep 21, 2020 · Artificial Intelligence

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

This article explains how keyword matching in search advertising works, outlines the challenges of semantic gaps, matching‑mode determination and scalability, and describes data‑driven synonym transformation techniques—including rule‑based, sequence‑to‑sequence, metric‑space and graph‑based models—to improve recall, efficiency, and robustness.

Search AdvertisingSemantic Searchad tech

0 likes · 18 min read

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

Ctrip Technology

Apr 30, 2020 · Artificial Intelligence

Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions

This article presents a comprehensive overview of automated techniques for generating high‑quality search engine advertising keywords, covering background, traditional manual methods, intelligent keyword expansion using NLP, segmentation, POS tagging, BILSTM‑CRF, BERT classification, semantic matching with DSSM, and additional approaches such as query suggestion and synonym rewriting.

BERTBILSTM-CRFNLP

0 likes · 15 min read

Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions

DataFunTalk

Jul 16, 2019 · Artificial Intelligence

Search Advertising and Ad Recall: Business Logic, Semantic Relevance, and Deep Learning Models at 360

This article explains the architecture of 360's search advertising system, detailing its ad recall, ranking, and display modules, illustrates exact‑match and semantic recall methods with a case study, and reviews the evolution from feature‑engineered GBDT models to deep learning approaches such as DSSM, ESIM, and BERT, including data preparation, training, and performance evaluation.

BERTDSSMSearch Advertising

0 likes · 10 min read

Search Advertising and Ad Recall: Business Logic, Semantic Relevance, and Deep Learning Models at 360

DataFunTalk

Jan 23, 2019 · Artificial Intelligence

Deep Learning Technologies Applied to Sogou Search Advertising

This talk by Sogou search advertising researcher Shupeng explains how deep learning techniques are applied to search ad tasks such as automated creative generation and click‑through‑rate prediction, covering system workflow, data pipelines, model evolution from linear models to Wide&Deep and NFM, evaluation metrics, and future directions.

CTR estimationSearch Advertisingautomated creative

0 likes · 33 min read

Deep Learning Technologies Applied to Sogou Search Advertising

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 predictionFeature Engineering

0 likes · 15 min read

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

Ctrip Technology

Jul 29, 2016 · Artificial Intelligence

Applying Deep Learning to Sogou Mobile Search Advertising: Multi‑Model Fusion for CTR Prediction

This article presents how deep learning techniques are applied to Sogou's mobile search advertising, detailing the system architecture, feature design, multi‑model fusion strategies, engineering implementation, evaluation metrics, and future directions for improving CTR prediction performance.

CTR predictionFeature EngineeringModel Fusion

0 likes · 13 min read