This article presents JD.com's end‑to‑end intelligent creative system, covering the background of content‑driven e‑commerce, a multi‑stage content understanding pipeline, AI‑powered video, image and copy generation, multimodal creative selection and distribution, and real‑world business impact.
This article details OPPO's commercial advertising targeting evolution, covering the background and logic, the multi‑layer targeting system and data modeling, automated intelligent targeting methods, the shift to untargeted crowd recall, and future considerations for ad‑targeting technology.
This article explains why pre‑testing brand advertisements is crucial, outlines a three‑dimensional metric system—information transmission, ad penetration, and persuasion—and shows how to apply these indicators through user research methods to evaluate and improve creative effectiveness.
The Shopee Off‑Platform Ads team built a GPU‑accelerated Creative Rendering System that uses a four‑layer architecture, CGO‑bridged C/C++ kernels, and template caching to process billions of product images daily, achieving roughly ten‑fold speedup, half the cost, and far reduced rack space while handling high concurrency.
The DataFun Summit 2022 features an Experimental Science and Causal Inference forum where leading data scientists from Didi, Tencent, Google, ByteDance, and others present deep technical talks on causal inference methods, A/B testing, game operations, and advertising experiments, offering practical insights and audience takeaways.
This article explores the role of ReRank in recommendation and advertising pipelines, detailing its algorithmic position, the challenges of diversity versus relevance, evaluation metrics such as DCG/NDCG, the evolution from heuristic methods to deep learning models, and practical insights from industry cases like Airbnb and Alibaba.
This article analyzes Meituan's delivery advertising system, detailing the shift from linear programming to an evolutionary‑strategy‑based multi‑action allocation (ES‑MACA), describing problem formalization, offline training, reward evaluation, online decision flow, extensive offline and online experiments, and future directions toward reinforcement learning.
This article introduces the AIDA framework for Alibaba's display advertising, detailing the business background, multi‑objective optimization challenges, the design of Deep GSP and Neural Auction mechanisms powered by deep learning and reinforcement learning, and outlines future technical and platform directions while also announcing recruitment opportunities.
The article explains what a mobile advertising SDK is, outlines its core components such as documentation, interfaces, logs, and samples, and details comprehensive testing approaches—including functional, compatibility, network, security, stability, and performance tests—organized in layered strategies from demo to code-level automation.
Hello Travel tackles information‑flow advertising challenges by using LightGBM‑based models to predict order conversion, creative performance, and pre‑bid user quality, augmenting sparse data with feature engineering and uplift techniques, while planning future fully automated delivery, richer pre‑screening, and cross‑channel reinforcement‑learning enhancements.
This talk explains how Hello Travel tackles challenges in information‑flow advertising by describing the market landscape, their business background, and detailed algorithmic optimization across plan, creative, and pre‑bid dimensions, including data‑driven modeling, feature engineering, LightGBM and uplift models, and outlines future directions.
The article analyzes the shortcomings of current click‑through‑rate benchmarks, explains why leaderboards are valuable, and proposes concrete criteria—including online evaluation, sequential test data, leakage prevention, and read‑only submissions—to build a more realistic and robust CTR benchmarking platform.
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.
To overcome the limited store‑candidate pool in Meituan’s location‑based in‑store advertising, the authors propose a heterogeneous mixing system that expands candidates with product ads, uses a shared‑representation network and Transformer‑based combination estimator, and applies a Thompson‑sampling cold‑start strategy, boosting RPM by up to 15 % while keeping latency low.
The article analyzes the rapid rise of ad‑supported streaming (AVOD) in the United States, highlighting Tubi's 2021 viewership surge, pandemic‑driven user behavior shifts, demographic trends, advertising revenue growth, and future content and hiring plans.
This article presents Beike's experience in applying intelligent advertising to the real‑estate industry, detailing business background, key technical challenges, multi‑task conversion modeling, GEO user targeting, delayed‑feedback calibration, and ROI‑driven budget allocation to improve ad effectiveness.
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.
By redesigning Hologres tables with column orientation, shard‑controlled Table Groups, distribution and clustering keys, adding bitmap indexes, refreshing statistics, caching external data, and tuning optimizer join order and resource scaling, Alibaba’s Mom advertising inventory system cut query latency by up to 35 % and memory use by 98 %, achieving a 5‑10× performance boost.
This article describes the end‑to‑end multimodal modeling pipeline—covering text, image, and video understanding, model evolution from shallow to deep networks, key‑frame extraction, fine‑tuning, and multimodal fusion—used in Tencent's game ad exchange platform, along with practical deployment challenges and solutions.
This article details YiBei's advertising data platform built on ClickHouse, covering business requirements, why ClickHouse was chosen over Druid, storage engine and compression choices, real‑time and offline ingestion pipelines, partitioning, Zookeeper bottlenecks, atomic data replacement, and testing and release strategies for a high‑throughput, low‑latency ad analytics system.
Marketing Mix Modeling (MMM) is a statistical, macro‑level technique that quantifies the sales impact of diverse marketing, pricing, competitor, and economic factors, enabling brands to allocate budgets, simulate scenarios, and optimize ROI across online and offline channels, often complemented by attribution models for detailed digital insights.
The team built a lightweight AI‑FAAS platform that migrates C++ ad‑algorithm services to a Java‑centric, cloud‑native environment, encapsulates core operators as DolphinSQL plugins, and defines real‑time feature pipelines via standardized SQL, cutting service creation from weeks to days, enabling rapid iteration, high operator reuse, and near‑instant feature delivery.
This article presents a comprehensive overview of using reinforcement learning to solve advertising bidding ROI optimization, covering historical foundations, methodological reasoning, system architecture, practical implementation details, challenges, evaluation metrics, and recommended algorithms for real‑world ad placement scenarios.
This article details the design and implementation of an advertising data platform at eBay, explaining the business scenario, why ClickHouse was chosen over alternatives, the technical challenges faced, and the solutions involving lambda architecture, table engine choices, compression techniques, data ingestion pipelines, consistency guarantees, and deployment practices.
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.
This article introduces model‑free reinforcement learning fundamentals, reviews mainstream solution methods such as Monte‑Carlo, Temporal‑Difference, and n‑step TD with eligibility traces, discusses their application in online advertising and presents Tencent's game advertising practice, including algorithm choices, reward design, and experimental results.
This article compares recommendation, advertising, and search algorithms, explaining how their core problems, optimization objectives, model architectures, auxiliary strategies, and system-level challenges differ, with insights drawn from the author’s years of experience in each field.
The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.
The Elastic Federated Learning Solution (EFLS) is Alibaba’s open‑source platform that enables privacy‑preserving vertical and horizontal federated learning for large‑scale sparse advertising, offering data‑intersection, high‑performance C++ training, a visual console, novel aggregation algorithms, and a roadmap toward multi‑party scaling and advanced encryption.
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.
This article examines the business storage requirements of large‑scale advertising platforms and details how BaikalDB was architected as a cloud‑native, MySQL‑compatible distributed database that combines OLTP, OLAP, KV and full‑text capabilities while ensuring high reliability, low cost and linear scalability.
This article reviews the evolution of coarse‑ranking in large‑scale ad systems, explains the two main technical routes—set selection and precise value estimation—introduces the Computing‑Power‑Cost‑Aware Online Lightweight Deep (COLD) pre‑ranking framework, and presents experimental results and future directions for deeper integration with fine‑ranking.
The article describes a three‑layer community advertising placement system for Xianyu, detailing its four core processes (recruitment, selection, construction, delivery), resource slots, material templates, task definition, data storage, real‑time monitoring, and successful case studies, while outlining future algorithmic enhancements.
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.
The 2021 Tencent Advertising Algorithm Competition, held as the ACM MM 2021 Grand Challenge, announced the top three teams for two tracks, presented the accepted multimodal video advertising papers with detailed reviewer comments, and highlighted the significance of algorithmic innovation over ranking alone.
This article explains how Hulu leverages machine learning and AI techniques such as ad targeting, inventory prediction, flow matching, conversion rate optimization, causal inference, and shared‑account detection to improve the efficiency, effectiveness, and revenue of its video advertising ecosystem.
This article presents a comprehensive overview of Alibaba's intelligent marketing advertising platform, covering business fundamentals, microservice migration, service governance, database design, real‑time data transmission, billing, reporting, and performance optimizations for high‑throughput, low‑latency ad delivery.
The Dynamic Descriptive Model (DDM) introduces a scalable pipeline that automatically harvests product assets, perceives their visual attributes, encodes designers’ expertise in an extended SVG‑based descriptive language, and generates high‑quality, native‑looking ad creatives at massive scale, delivering 5‑80 % CTR gains and tens of millions of daily outputs.
This article presents iQIYI's effect advertising system, detailing its dual-engine resource layout, oCPX billing model, algorithmic challenges of high‑dimensional sparse conversion data, the multi‑stage personalized recommendation pipeline, eCPM‑based ranking, online training/inference workflow, and intelligent bidding strategies that balance cost control and traffic quality.
This article analyzes the bottlenecks of advertising coarse‑ranking training on the Light framework and presents a series of optimizations—including parallel data download, thread‑queue buffering, integer‑to‑string conversion with fmt, and zlib replacement with czlib—that together achieve up to 58% QPS improvement and notable CPU efficiency gains.
The article examines how increasing privacy regulations—especially after iOS 14—challenge personalized advertising by focusing on the technical core of user identification, the risks of cross‑domain data sharing, and a range of mitigation strategies such as fingerprint protection, third‑party cookie blocking, and privacy‑preserving attribution frameworks like PCM, SKAdNetwork, AEM, and FLoC.
The article outlines Youzan’s Demand‑Side Platform, detailing its ad channels, placement and bidding strategies, data tracking and order attribution mechanisms, and multi‑channel feedback, while highlighting testing difficulties such as Kafka‑driven tracking, link parameter loss, UUID inconsistencies, and the need for robust third‑party API monitoring.
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.
This article explains the fundamentals of online advertising business, outlines its technical challenges such as high concurrency, complex logic, and massive data handling, and then details a practical architecture—including data storage, indexing, retrieval, billing, and reporting—offering actionable insights for building robust ad platforms.
This article explains the theoretical foundations of CTR modeling, why click‑through rates are intrinsically unpredictable at the micro level, the simplifying assumptions that make binary classification feasible, and how evaluation metrics like AUC, contradictory samples, theoretical AUC bounds, and calibration affect model performance.
This article explains how causal inference techniques are applied to model the uplift effect of ad placement in Alibaba's Fliggy search CPC advertising, transforming budget allocation into a multi‑objective optimization problem and describing practical control methods, feature engineering, sample re‑sampling, model designs, uplift evaluation, and future research directions.
This article reviews the limitations of traditional DMP and AI‑driven intelligent targeting in e‑commerce, introduces JD.com's Smart DMP framework that combines merchant intent with high‑relevance modeling, and presents experimental results showing over 15% CTR improvement and widespread merchant adoption.
This article systematically introduces Alibaba's advertising intelligence platform, covering the evolution from basic CPM/CPC models to advanced OCPC/OCPM, budget‑constrained bidding, multi‑constraint bidding, sequence‑based long‑term value bidding, multi‑channel allocation, and the AI‑driven Smart Bidding product, highlighting algorithmic foundations, practical implementations, and performance gains.
This article reviews the development history, technical routes, and recent breakthroughs of pre‑ranking (coarse ranking) in large‑scale advertising systems, focusing on Alibaba's COLD (Computing‑power‑cost‑aware Online and Lightweight Deep) framework, its model design, engineering optimizations, experimental results, and future research directions.
This article presents a comprehensive overview of the 58.com alliance advertising platform, detailing its business model, core modules such as SSP, direct‑investment, DSP and creative platforms, media integration flow, performance optimizations, GC tuning, indexing strategies, and future directions for programmatic creative generation.
This article presents a comprehensive overview of the 58.com alliance advertising platform, detailing its business model, core modules such as SSP, DSP, media and creative platforms, system architecture, performance optimizations, big‑data processing, and future directions for programmatic creative generation.
This article explains the scale of online ad fraud, outlines common advertising billing models, describes how fake traffic generates revenue, defines invalid clicks, and presents a comprehensive anti‑fraud system that combines rule‑based methods, feature engineering, and AI models such as TextCNN, BiLSTM, BERT, Wide&Deep and GraphSage to identify and block fraudulent ad clicks.
This article describes how graph theory and graph convolutional neural networks are leveraged to model user‑IP relationships, detect fraudulent advertising clusters, and improve detection accuracy and recall through a combination of unsupervised graph algorithms and supervised GCN training in a large‑scale ad‑anti‑fraud system.
In this live talk, Dr. Zhang Renyu, an assistant professor at NYU Shanghai and economist at Kuaishou, presents his research on optimizing cold-start problems in feed advertising using a novel Shadow Bidding with Learning (SBL) algorithm, detailing its design, implementation, and experimental results.
On November 7, 2020, Hulu’s Shulei presented how the company’s video advertising platform employs AI-driven algorithms—including binge‑watch prediction, inventory forecasting with Prophet, and a PID‑controller based real‑time allocation system—to enhance targeting, optimize revenue, and improve user experience.
This recommendation highlights two iTech Forum pieces—one detailing iQiyi’s effect advertising exploration and implementation, and another documenting the company’s Druid practice and technical evolution—providing readers with in‑depth case studies, performance insights, and practical guidance for similar large‑scale data and advertising systems.
This article explains how the classic PID feedback control algorithm can be adapted to regulate advertising bidding costs, describing its theory, component effects, practical implementation in a real‑time ad‑exchange, simulation experiments, and the resulting improvements in budget adherence and cost stability.
This article presents Tencent's growth platform, detailing challenges of multi‑channel traffic, material specifications, bidding strategies, and user segmentation, and explains the automated advertising system, RTA and DPA models, material generation pipelines, and the integrated pull‑to‑push approach for user acquisition and retention.
This article introduces Baidu's new multi‑objective recall system (MOBIUS), which integrates relevance and business metrics such as CPM into the recall stage by migrating CTR models to recall, using data augmentation and a teacher‑student framework to improve ad monetization while preserving relevance.
This article presents Alibaba's latest ranking innovations for directed e‑commerce advertising, detailing the challenges of long‑term user interest modeling, the Search‑Based Interest Model (SIM) that extends behavior sequences to ten thousand actions, and the Dynamic Computation Allocation Framework (DCAF) that optimizes per‑request compute resources to maximize system revenue.
This article describes how a commercial advertising team transformed manual ad‑recall testing into a semi‑automated regression workflow by building comparison, log, result, and case tools, addressing challenges such as anti‑fraud restrictions, data quality, special scenarios, and inter‑case interference.
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.
This article systematically introduces the advertising transmission process, outlines challenges of scale and stability, and presents a comprehensive set of architectural, operational, and performance optimizations—including primary‑backup high availability, stateless incremental processing, message reliability, flow tiering, indexing sharding, and schedule management—to achieve high‑throughput, low‑latency, and eventually consistent ad delivery.
The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.
The article presents a novel multi‑channel sequential advertising framework that models budget‑constrained GMV optimization as a dynamic knapsack problem, introduces a long‑term value‑based RL solution (MSBCB), and validates its superiority through extensive offline and online experiments showing up to 10% ROI improvement.
This article details the 2020 Tencent Advertising Algorithm Competition, describing the user profiling task, data fields, feature engineering, Python code for ID mapping and Word2Vec training, multiple model architectures (LSTM, CNN-Inception, transformer), and the final performance results achieved by the team.
The article details the Tencent Advertising Algorithm competition final, explains the chizhu team's approach of converting ad IDs into word sequences for text classification using large‑scale word2vec embeddings and a dual BiLSTM architecture, presents custom loss functions, training tricks, and shares full Python model code, achieving an overall rank of 11.
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.
The article details JD.com's advertising division's comprehensive backend engineering efforts—including traffic handling, data pipeline upgrades, memory optimization, and disaster‑recovery designs—to ensure system stability and performance during the high‑traffic 618 sales event.
This article explains how Weibo's advertising platform leverages content understanding—covering system architecture, problems caused by insufficient comprehension, the construction of NLP and vision capabilities, content‑based ad strategies, and a celebrity‑brand knowledge graph—to improve ad relevance and ROI.
The article explains how advertising inventory forecasting—predicting how many users will see a specific ad—poses challenges due to fluctuating traffic and user segmentation, and describes a new deep spatial‑temporal tensor factorization model that dramatically improves prediction accuracy, scalability, and robustness for large‑scale ad platforms.
This article shares a competition winner's step‑by‑step solution for predicting user age and gender from ad click sequences, treating IDs as words, using word2vec embeddings, a custom transformer‑LSTM model, dual‑task loss, and weight‑search post‑processing.
This article explains the economic foundations of optimal auction mechanisms in online advertising, covering virtual valuations, market reserve price, dynamic reserve price, price‑compression factors, and practical budget‑pacing techniques used by platforms such as LinkedIn and Yahoo to balance spend and performance.
The 2020 Tencent Advertising Algorithm Competition challenges participants to predict user gender and age from 90‑day click logs, and the champion shares data understanding, feature engineering techniques such as one‑hot, TF‑IDF, Word2Vec, and modeling strategies including GBDT and RNN/LSTM/GRU to guide competitors.
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.
In an interview, iQIYI Live Cloud’s Chen Kunzhong explains how their WebRTC‑based RTCDN reduces encoding and transmission delay to achieve roughly 100 ms end‑to‑end latency for cloud‑gaming ads, supporting cross‑device play, instant click‑to‑play sessions, and future 5G‑enhanced high‑resolution experiences.
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.
The iQIYI effect advertising platform offers in‑frame and out‑of‑frame slots, uses CPX and oCPX billing with real‑time eCPM bidding, and employs a three‑stage ranking funnel—recall, coarse‑ranking, and fine‑ranking—powered by dual‑tower, wide‑deep and multi‑task models, while smart‑bidding, cold‑start handling, and traffic‑level adjustments automate optimization and stabilize conversion costs.
This article explains how Youku combined user‑growth objectives with real‑time bidding (RTB) advertising, designing and optimizing DSP algorithms to maximize high‑value daily active users under cost constraints, covering business background, RTB mechanics, ranking models, pricing strategies, engineering pipelines, and future directions.
This article details the commercial strategy team's exploration of embedding technologies for a second‑hand car platform, covering mainstream embedding methods, their application in advertising recall and ranking pipelines, system architecture, model optimizations, evaluation results, and future directions.
iQIYI’s brand‑advertising system combines statistical and ARIMA‑based forecasting, adaptive and deep‑learning models, factorization‑machine regression, large‑scale bipartite‑graph allocation, hierarchical handling of long‑tail dimensions, frequency‑capping constraints, and an AI‑driven video‑tagging pipeline to accurately estimate inventory and dynamically place ads.
Kunpeng, a three‑level advertising framework for Xianyu, unifies activity‑scene‑material abstraction, extensible DataFetcher and modular filter libraries, and a global traffic‑mixing algorithm, enabling business groups to self‑service material delivery, achieve 60‑100% conversion gains, cut UV cost by ~40% and halve time‑to‑market.
This talk presents Alibaba's intelligent marketing platform, detailing its three ad systems, the massive data and low‑latency requirements, and the architectural solutions—including cold‑hot data separation, OLDB storage, full‑link tracing, elastic compute, and platform‑level componentization—that achieve high performance, high availability, and cost‑effective operation.
The article explains Weibo’s O‑Series advertising system, detailing its three‑part strategy of smart bidding, intelligent targeting, and ROI modeling, the underlying machine‑learning techniques such as deep‑FM, dual‑tower and PID control, and how these components optimize show, click, conversion rates and advertiser ROI.
This article presents a large‑scale distributed algorithm for guaranteed‑delivery advertising that incorporates user‑level personalization, solves a bipartite matching problem with complex constraints, and introduces a real‑time pacing module to smooth delivery while maximizing platform revenue and advertiser satisfaction.
Wedge is a Meituan‑Dianping advertising experiment configuration platform that provides extensible, flow‑based A/B testing with version control, real‑time monitoring, and a user‑friendly UI, enabling algorithm, engineering, and business teams to rapidly iterate, audit, and roll back complex vertical and horizontal experiments.
This article describes how an e‑commerce platform built a big‑data pipeline—including TTLog, Lindorm, BCP, MetaQ, Blink and Xflush—to detect and verify search advertising placement errors in real time, covering background, implementation details, core challenges, and future optimization directions.
The article details how JD’s advertising division tackled the massive traffic surge of the 11.11 shopping festival by expanding shard capacity, optimizing models and data pipelines, migrating workloads to the cloud, and implementing cost‑saving measures that together ensured stable, high‑performance ad delivery.
At a Tsinghua University forum, Hulu presented a comprehensive overview of its big‑data solutions for advertising and streaming, covering challenges of massive, complex data, the limits of MySQL, and advanced techniques using HBase, Protobuf, Redis batch pipelines, and its own MPP engine Nesto for high‑performance, scalable analytics.
This article details the 360 display advertising system's architecture and the progressive evolution of its recall module, covering business overview, overall pipeline, various recall strategies—including Boolean, vectorized, and deep‑tree approaches—and the performance optimizations applied to meet real‑time constraints.
The talk outlines how Tencent Advertising leverages deep‑learning visual algorithms—including GCN‑based edge refinement, template generation, AutoML‑driven smart review, and a dual‑tower click‑through‑rate model—to automate creative production, improve ad quality, and enhance user experience across creation, review, and playback stages.
This article presents iQIYI's effect advertising system, detailing its dual‑engine resource slots, oCPX billing model, algorithmic challenges of high‑dimensional sparse conversion data, the personalized recommendation pipeline, feature engineering across real‑time, short‑term and long‑term signals, and the intelligent bidding mechanism that balances cost control with traffic quality.
The article outlines a complete testing framework for advertising recall services, analyzing three demand types, defining testing focus for each, and presenting tools for log comparison, recall result verification, result comparison, and batch regression to ensure high‑quality ad delivery and revenue stability.
The presentation explains how Weibo leverages big‑data technologies, user profiling, and social‑first advertising models to drive commercial growth, detailing data‑driven product development, real‑time and offline data warehouses, scientific experiments, and case studies that illustrate the impact on revenue and user engagement.
This article presents a comprehensive overview of the evolution of Weibo's advertising strategy engineering architecture, detailing the system's growth from early banner ads to a sophisticated, multi‑layered online advertising platform that integrates algorithmic models, A/B experimentation, real‑time data pipelines, and precision targeting to support scalable, high‑performance ad delivery.
Mark Zuckerberg reflects on Facebook’s tumultuous 2018, detailing crises from data scandals to advertising missteps, while analyzing how the company’s vision, ad innovations, and five key success factors have shaped its business model and future challenges.
This article explains how Mogu’s advertising system designs and evolves a real‑time data pipeline—covering merchant and operation needs, data collection, cleaning, processing with Storm, Flink, and Kylin, and service guarantees—to enable high‑quality, low‑latency analytics for advertisers and the platform.
This article details how Hulu leverages machine learning and AI techniques—including ad targeting, inventory prediction, conversion rate optimization, causal inference, and real‑time bidding—to improve ad efficiency, user experience, and revenue across its video streaming platform.
iQIYI’s newly released interactive video advertising white paper outlines the market background, classification, creation techniques, and performance metrics of interactive video ads, showcasing how immersive two‑way ad experiences can boost brand communication and set new industry standards.
The article details a contestant's end‑to‑end approach for an advertising competition, covering data analysis, rule‑based preprocessing, a three‑layer neural network architecture, model‑rule ensemble weighting, self‑correction strategies for the B phase, and final model‑only solutions that achieved top scores.
