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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)

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.

AIBeam SearchLLM

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 intelligenceefficiency optimizationlarge language models

0 likes · 33 min read

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

Meituan Technology Team

Jul 4, 2024 · Artificial Intelligence

Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches

Meituan’s search advertising has progressed from rule‑based keyword mining to hierarchical recall that partitions traffic and supply, and now to generative recall using large language models, chain‑of‑thought generation, diffusion‑enhanced multimodal vectors, and knowledge distillation, expanding the decision space while tackling compute and ROI challenges.

Generative ModelsMeituanMultimodal Retrieval

0 likes · 19 min read

Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches

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.

AIErniead satisfaction

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.

AIGCBaidularge language models

0 likes · 22 min read

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.

Deep Learninghierarchical modelingsearch advertising

0 likes · 22 min read

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

ITPUB

Sep 15, 2022 · Artificial Intelligence

Why Precise Feature Engineering Still Matters in Recommendation Systems

In the era of deep learning, feature engineering remains crucial for recommendation and search advertising because it bridges raw relational data and models, improves performance, reduces complexity, and handles high‑cardinality, large‑scale, and time‑sensitive scenarios with robust transformations and statistical encoding.

AIRecommendation Systemsdata preprocessing

0 likes · 20 min read

Why Precise Feature Engineering Still Matters in Recommendation Systems

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.

CTRMobiusad 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.

AIRecommendation Systemsdata preprocessing

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%.

Distributed TrainingEuler frameworkgraph embeddings

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.

AIbinary retrievalhierarchical bidding graph

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.

Recommendation Systemscold startheterogeneous graph neural network

0 likes · 14 min read

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

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.

effectivenessfeature selectionpre‑ranking

0 likes · 15 min read

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

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.

embedding layersfeature selectionsearch advertising

0 likes · 12 min read

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

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.

Ad Techkeyword matchingmachine learning

0 likes · 18 min read

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

Meituan Technology Team

Aug 6, 2020 · Artificial Intelligence

Meituan SIGIR2020 Workshop: MT‑BERT, KDD Cup Solutions, and Knowledge Graph Applications

At the SIGIR 2020 Meituan workshop, researchers unveiled MT‑BERT’s large‑scale pre‑training and compression techniques, a KDD Cup winning solution that tackles bias with graph‑ and multimodal learning for search advertising, and a massive food‑delivery knowledge graph powering personalized recommendations, all demonstrating significant real‑world performance gains.

Multimodal Learningmodel compressionpretrained language models

0 likes · 18 min read

Meituan SIGIR2020 Workshop: MT‑BERT, KDD Cup Solutions, and Knowledge Graph Applications

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

Alibaba Cloud Developer

Apr 28, 2020 · Big Data

How Alibaba Tests Big Data AI Applications: Six Challenges and Solutions

This article explains how Alibaba's search, recommendation, and advertising platforms handle the unique quality challenges of big‑data AI applications, detailing six major testing problems and the comprehensive strategies—including functional, real‑time, performance, and stability testing—used to ensure reliable online services.

AI testingBig DataDevOps

0 likes · 27 min read

How Alibaba Tests Big Data AI Applications: Six Challenges and Solutions

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.

BERTDSSMad recall

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 estimationautomated creativemachine learning

0 likes · 33 min read

Deep Learning Technologies Applied to Sogou Search Advertising

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 predictionDeep LearningModel Fusion

0 likes · 13 min read