CIKM 2013 Tutorial: Real-time Bidding: A New Frontier of Computational Advert...Shuai Yuan
Computational Advertising has been an important topical area in information retrieval and knowledge management. This tutorial will be focused on real-time advertising, aka Real-Time Bidding (RTB), the fundamental shift in the field of computational advertising. It is strongly related to CIKM areas such as user log analysis and modelling, information retrieval, text mining, knowledge extraction and management, behaviour targeting, recommender systems, personalization, and data management platform.
This tutorial aims to provide not only a comprehensive and systemic introduction to RTB and computational advertising in general, but also the emerging research challenges and research tools and datasets in order to facilitate the research. Compared to previous Computational Advertising tutorials in relevant top-tier conferences, this tutorial takes a fresh, neutral, and the latest look of the field and focuses on the fundamental changes brought by RTB.
We will begin by giving a brief overview of the history of online advertising and present the current eco-system in which RTB plays an increasingly important part. Based on our field study and the DSP optimisation contest organised by iPinyou, we analyse optimization problems both from the demand side (advertisers) and the supply side (publishers), as well as the auction mechanism design challenges for Ad exchanges. We discuss how IR, DM and ML techniques have been applied to these problems. In addition, we discuss why game theory is important in this area and how it could be extended beyond the auction mechanism design.
CIKM is an ideal venue for this tutorial because RTB is an area of multiple disciplines, including information retrieval, data mining, knowledge discovery and management, and game theory, most of which are traditionally the key themes of the conference. As an illustration of practical application in the real world, we shall cover algorithms in the iPinyou global DSP optimisation contest on a production platform; for the supply side, we also report experiments of inventory management, reserve price optimisation, etc. in production systems.
We expect the audience, after attending the tutorial, to understand the real-time online advertising mechanisms and the state of the art techniques, as well as to grasp the research challenges in this field. Our motivation is to help the audience acquire domain knowledge and obtain relevant datasets, and to promote research activities in RTB and computational advertising in general.
Demand Side Platforms: Silver Bullet or Fog of War?Matt Hunter
This concise presentation explores a simplified view of the digital advertising ecosystem, explaining the role of DSPs and their impact on publishers, agencies and advertisers.
Evolution of online advertising technologies explained. Only main technologies like Ad exchange, DSP, SSP etc. are covered. There are many other technologies I'm planning to explain on another presentation
카카오의 광고지능 (Intelligence on Kakao Advertising)if kakao
정부환(ben.hur) / kakao
---
온라인 광고는 현재 인터넷 비즈니스 모델을 가능케하는 핵심 동인이다. 새로운 서비스가 등장하고 시장 규모가 커짐에 따라서 온라인 광고 생태계도 함께 진화하고 여러 기술적 도전이 있다. 지면 중심에서 오디언스 중심으로, 단순 광고 노출과 클릭에서 광고주의 필요를 반영한 다양한 전환으로, 그리고 수의 계약에서 실시간 자동 입찰로 온라인 광고 생태계가 진화하고 있다. 이런 변화의 흐름에서 여러 지면에 접속하는 사용자들의 성향을 즉시 분석해서 가장 적합한 광고를 실시간으로 선택하고 노출하는 것은 기술적으로 매우 어려운 문제다. 본 발표는 카카오의 광고 랭킹에 필요한 데이터와 알고리즘을 간략히 소개한다.
-------------
<Glossary>
- Audience: 광고 도메인에서 사용자 (User)를 뜻함
- Publisher: 앱이나 웹 등의 광고 지면(inventory)/매체(media)을 제공하는 사람
- SSP: Supplier-side platform
- DSP: Demand-side platform
- DMP: Data management platform
- MAT: Mobile app tracking
- Pixel: 광고의 전화을 추척하기 위해서 웹에 심어두는 스크립트
- AdX: Ad Exchange, 광고의 mediation이 이뤄지는 마켓
- RTB: Real-time bidding
- Programmatic buying: AdX에서 프로그램에 의해서 (자동으로) 광고 입찰 및 낙찰이 이뤄지는 것
- Impression: 광고 노출
- ROAS: Return on ad spending
- eCPM: effective cost per mille (1천회 노출당 기대 비용/수익)
- CPM/CPC/CPA: Cost Per mille/click/acquisition(action, conversion)
- CTR/CVR: Clickthrough rate, (post-click) conversion rate
- SGD: Stochastic gradient descent
- FTRL: Follow-the-regularized-leader
- FM/FFM/FwFM: Factorization machines / Field-aware FM / Field-weighted FM
- DCN: Deep & cross network
- LDA: Latent dirichlet allocation
- DNN: Deep neural network (DL)
- AE: Auto-encoder
- GBDT: Gradient-boosting decision tree
- Targeting: 광고주가 자신의 광고가 노출될 오디언스 (사용자)를 제한하는 것
- Retargeting: 사용자의 특정 행동 (i.e., 광고주 사이트 방문)에 반응해서 광고를 노출/제한하는 것
- LookALike: 광고주가 제공한 오디언스 그룹과 유사한 특징을 갖는 오디언스군 (유사확장타게팅)
- PPC: Pay per click 클릭당 과금액
- RIG: Relative information gain
- NE: Normalized entropy
- AUC: Area under ROC Curve
- GSP/VCG: Generalized second price auction / Vickrey-Clarke-Groves auction
- DNT: Do not track
The process that happens between the time a site visitor clicks a link and a page loads with targeted ads can seem mysterious – but this infographic sheds light on what goes on behind the scenes of programmatic advertising and the technology that brings your message to your buyers.
CIKM 2013 Tutorial: Real-time Bidding: A New Frontier of Computational Advert...Shuai Yuan
Computational Advertising has been an important topical area in information retrieval and knowledge management. This tutorial will be focused on real-time advertising, aka Real-Time Bidding (RTB), the fundamental shift in the field of computational advertising. It is strongly related to CIKM areas such as user log analysis and modelling, information retrieval, text mining, knowledge extraction and management, behaviour targeting, recommender systems, personalization, and data management platform.
This tutorial aims to provide not only a comprehensive and systemic introduction to RTB and computational advertising in general, but also the emerging research challenges and research tools and datasets in order to facilitate the research. Compared to previous Computational Advertising tutorials in relevant top-tier conferences, this tutorial takes a fresh, neutral, and the latest look of the field and focuses on the fundamental changes brought by RTB.
We will begin by giving a brief overview of the history of online advertising and present the current eco-system in which RTB plays an increasingly important part. Based on our field study and the DSP optimisation contest organised by iPinyou, we analyse optimization problems both from the demand side (advertisers) and the supply side (publishers), as well as the auction mechanism design challenges for Ad exchanges. We discuss how IR, DM and ML techniques have been applied to these problems. In addition, we discuss why game theory is important in this area and how it could be extended beyond the auction mechanism design.
CIKM is an ideal venue for this tutorial because RTB is an area of multiple disciplines, including information retrieval, data mining, knowledge discovery and management, and game theory, most of which are traditionally the key themes of the conference. As an illustration of practical application in the real world, we shall cover algorithms in the iPinyou global DSP optimisation contest on a production platform; for the supply side, we also report experiments of inventory management, reserve price optimisation, etc. in production systems.
We expect the audience, after attending the tutorial, to understand the real-time online advertising mechanisms and the state of the art techniques, as well as to grasp the research challenges in this field. Our motivation is to help the audience acquire domain knowledge and obtain relevant datasets, and to promote research activities in RTB and computational advertising in general.
Demand Side Platforms: Silver Bullet or Fog of War?Matt Hunter
This concise presentation explores a simplified view of the digital advertising ecosystem, explaining the role of DSPs and their impact on publishers, agencies and advertisers.
Evolution of online advertising technologies explained. Only main technologies like Ad exchange, DSP, SSP etc. are covered. There are many other technologies I'm planning to explain on another presentation
카카오의 광고지능 (Intelligence on Kakao Advertising)if kakao
정부환(ben.hur) / kakao
---
온라인 광고는 현재 인터넷 비즈니스 모델을 가능케하는 핵심 동인이다. 새로운 서비스가 등장하고 시장 규모가 커짐에 따라서 온라인 광고 생태계도 함께 진화하고 여러 기술적 도전이 있다. 지면 중심에서 오디언스 중심으로, 단순 광고 노출과 클릭에서 광고주의 필요를 반영한 다양한 전환으로, 그리고 수의 계약에서 실시간 자동 입찰로 온라인 광고 생태계가 진화하고 있다. 이런 변화의 흐름에서 여러 지면에 접속하는 사용자들의 성향을 즉시 분석해서 가장 적합한 광고를 실시간으로 선택하고 노출하는 것은 기술적으로 매우 어려운 문제다. 본 발표는 카카오의 광고 랭킹에 필요한 데이터와 알고리즘을 간략히 소개한다.
-------------
<Glossary>
- Audience: 광고 도메인에서 사용자 (User)를 뜻함
- Publisher: 앱이나 웹 등의 광고 지면(inventory)/매체(media)을 제공하는 사람
- SSP: Supplier-side platform
- DSP: Demand-side platform
- DMP: Data management platform
- MAT: Mobile app tracking
- Pixel: 광고의 전화을 추척하기 위해서 웹에 심어두는 스크립트
- AdX: Ad Exchange, 광고의 mediation이 이뤄지는 마켓
- RTB: Real-time bidding
- Programmatic buying: AdX에서 프로그램에 의해서 (자동으로) 광고 입찰 및 낙찰이 이뤄지는 것
- Impression: 광고 노출
- ROAS: Return on ad spending
- eCPM: effective cost per mille (1천회 노출당 기대 비용/수익)
- CPM/CPC/CPA: Cost Per mille/click/acquisition(action, conversion)
- CTR/CVR: Clickthrough rate, (post-click) conversion rate
- SGD: Stochastic gradient descent
- FTRL: Follow-the-regularized-leader
- FM/FFM/FwFM: Factorization machines / Field-aware FM / Field-weighted FM
- DCN: Deep & cross network
- LDA: Latent dirichlet allocation
- DNN: Deep neural network (DL)
- AE: Auto-encoder
- GBDT: Gradient-boosting decision tree
- Targeting: 광고주가 자신의 광고가 노출될 오디언스 (사용자)를 제한하는 것
- Retargeting: 사용자의 특정 행동 (i.e., 광고주 사이트 방문)에 반응해서 광고를 노출/제한하는 것
- LookALike: 광고주가 제공한 오디언스 그룹과 유사한 특징을 갖는 오디언스군 (유사확장타게팅)
- PPC: Pay per click 클릭당 과금액
- RIG: Relative information gain
- NE: Normalized entropy
- AUC: Area under ROC Curve
- GSP/VCG: Generalized second price auction / Vickrey-Clarke-Groves auction
- DNT: Do not track
The process that happens between the time a site visitor clicks a link and a page loads with targeted ads can seem mysterious – but this infographic sheds light on what goes on behind the scenes of programmatic advertising and the technology that brings your message to your buyers.
Growth Hacking / Marketing 101: It's about processRuben Hamilius
Outline of the repeatable growth process startups should adopt to do Growth Marketing. Show & tell deck on basic principles and mindsets of Growth hacking for early stage startups.
Presented at the Singtel Group-Samsung Regional Mobile App Challenge 2015
in the Startup Mentorship Programme
Building a Repeatable, Scalable & Profitable Growth ProcessDavid Skok
In a talk I gave at SaaS North 2017 conference in Ottawa, I talk about the fundamentals of building a repeatable, scalable, and profitable growth process for a startup.
Header bidding - Everything you need to knowAutomatad Inc.
Header Bidding is an advanced programmatic technique which allows you to call multiple demand partners or bidders to bid on your ad inventory, before sending the ad request to the Ad server.
This increases CPMs and Yield of your inventories. It has been used by 71% of the US publishers and proven method of increasing your Ad revenue by 60%.
In this slide, you'll learn 'What is Header Bidding', 'How header bidding works', the current state of the technology, and more.
At Automatad Inc, we've deployed our 'Header Bidding' solution to 100+ publishers across the globe and seen a drastic increase in the revenue.
In the presentation we cover how it's important to track the
key metrics within a SaaS sales funnel and how to optimize them at each stage.
Many SaaS tools look at just number of sales and there are many stages both before and after the sale in which saas tools can work more efficiently and produce better results.
We also cover communication strategies and the important of using the behaviour of your consumers to model your sales strategy.
Learn the importance of Keyword Research and competition analysis for SEO.
Keyword Research is a process of finding the good keywords for website to optimize the site.
Competition Analysis is a process of finding the difficulty level of keywords compared with existing competition for keywords.
SaaS Marketing Plan: 5 Ways to Get your B2B App to Sell ItselfLincoln Murphy
Your B2B SaaS app will not sell itself unless it is designed to do so. This is a guide to getting your B2B SaaS to 'sell itself' was created by noted SaaS Marketing expert and Growth Hacker Lincoln Murphy of Sixteen Ventures.
This guide is full of actionable, unique, and thought-provoking ideas - Growth Hacks if you will - that are designed to allow your product to sell itself.
From attracting the right audience and driving user engagement, to making it easy to buy and easy to share through orchestrated virality, this guide covers all aspects of what's necessary to drive growth with SaaS companies of any stage and serving any market.
Whether you have a self-service sales model or one that requires outside sales reps to drive business, the tips and techniques contained in this guide and the source blog post will help you achieve your goals of efficiently scaling your sales process.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Zero to 100 - Part 1: Intro + First SectionDavid Skok
Zero to 100 is a learning program from David Skok. It is a detailed instruction manual for how to take your startup from zero to $100m, with a particular focus on the area of building a go-to-market machine. So many of today’s founders come from a product or technical background, and have never been involved with sales and marketing. Right after starting their venture, they are hit with the huge problem of how to build their go-to-market organization and processes. It breaks the journey down into 9 steps, and explains why it is crucial not to skip steps in this journey in the rush to get ahead. The major emphasis of the course focuses on building a repeatable, scalable and profitable growth machine. Once you have that in place, you are ready to hit the gas and scale like crazy.
To see videos of the presentations, click here: https://www.forentrepreneurs.com/matrix-growth-academy-zero-to-100-videos/
Building a Sales and Marketing machine for a B2B software company involves many functions working together. This slide deck explores the process of funnel design that is highly buyer centric. It looks at the Buyer journey, and how to successfully construct a buying process that they buyer will enjoy going through, which is very different from the typical sale process that is designed from the vendors standpoint, and fails because the buyer is not motivated to go through the steps.
WebSummit 2018 - The SaaS Business Model & MetricsDavid Skok
SaaS businesses are extremely sensitive to a small number of important variables. If you are running a SaaS company, understanding how these variables drive your business model is crucial to long-term success. In this talk, David Skok, author of the now famous SaaS Metrics 2.0 blog post will talk through those key metrics and their impact on the overall SaaS business model.
Sales leader Derek Draper shares the Stage Management Guide that boosted his team's performance metrics across the board. Read the rest of the story on First Round Review: http://firstround.com/review/this-sales-plan-moves-the-needle-on-every-success-metric/
In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. The ability of making impression level bid decision and targeting to an individual user in real-time has fundamentally changed the landscape of the digital media. The further demand for automation, integration and optimisation in RTB brings new research opportunities in the IR fields, including information matching with economic constraints, CTR prediction, user behaviour targeting and profiling, personalised advertising, and attribution and evaluation methodologies. In this tutorial, teamed up with presenters from both the industry and academia, we aim to bring the insightful knowledge from the real-world systems, and to provide an overview of the fundamental mechanism and algorithms with the focus on the IR context. We will also introduce to IR researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.
Machine learning for profit: Computational advertising landscapeSharat Chikkerur
Online advertising is a multibillion-dollar industry than spans several subfields of computer science and engineering including information retrieval, machine learning, auction theory, optimization and distributed computing. Competitiveness in the marketplace is directly dependent on accuracy, scalability and sophistication of machine learning algorithms involved. The talk will present an overview of the computational advertising landscape, outline several key challenges, and discuss how machine learning addresses them.
http://www.buffalo.edu/cdse/CDSEdayslanding1/cdse-days2017/faculty_directory/SharatChikkerur.html
Growth Hacking / Marketing 101: It's about processRuben Hamilius
Outline of the repeatable growth process startups should adopt to do Growth Marketing. Show & tell deck on basic principles and mindsets of Growth hacking for early stage startups.
Presented at the Singtel Group-Samsung Regional Mobile App Challenge 2015
in the Startup Mentorship Programme
Building a Repeatable, Scalable & Profitable Growth ProcessDavid Skok
In a talk I gave at SaaS North 2017 conference in Ottawa, I talk about the fundamentals of building a repeatable, scalable, and profitable growth process for a startup.
Header bidding - Everything you need to knowAutomatad Inc.
Header Bidding is an advanced programmatic technique which allows you to call multiple demand partners or bidders to bid on your ad inventory, before sending the ad request to the Ad server.
This increases CPMs and Yield of your inventories. It has been used by 71% of the US publishers and proven method of increasing your Ad revenue by 60%.
In this slide, you'll learn 'What is Header Bidding', 'How header bidding works', the current state of the technology, and more.
At Automatad Inc, we've deployed our 'Header Bidding' solution to 100+ publishers across the globe and seen a drastic increase in the revenue.
In the presentation we cover how it's important to track the
key metrics within a SaaS sales funnel and how to optimize them at each stage.
Many SaaS tools look at just number of sales and there are many stages both before and after the sale in which saas tools can work more efficiently and produce better results.
We also cover communication strategies and the important of using the behaviour of your consumers to model your sales strategy.
Learn the importance of Keyword Research and competition analysis for SEO.
Keyword Research is a process of finding the good keywords for website to optimize the site.
Competition Analysis is a process of finding the difficulty level of keywords compared with existing competition for keywords.
SaaS Marketing Plan: 5 Ways to Get your B2B App to Sell ItselfLincoln Murphy
Your B2B SaaS app will not sell itself unless it is designed to do so. This is a guide to getting your B2B SaaS to 'sell itself' was created by noted SaaS Marketing expert and Growth Hacker Lincoln Murphy of Sixteen Ventures.
This guide is full of actionable, unique, and thought-provoking ideas - Growth Hacks if you will - that are designed to allow your product to sell itself.
From attracting the right audience and driving user engagement, to making it easy to buy and easy to share through orchestrated virality, this guide covers all aspects of what's necessary to drive growth with SaaS companies of any stage and serving any market.
Whether you have a self-service sales model or one that requires outside sales reps to drive business, the tips and techniques contained in this guide and the source blog post will help you achieve your goals of efficiently scaling your sales process.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Zero to 100 - Part 1: Intro + First SectionDavid Skok
Zero to 100 is a learning program from David Skok. It is a detailed instruction manual for how to take your startup from zero to $100m, with a particular focus on the area of building a go-to-market machine. So many of today’s founders come from a product or technical background, and have never been involved with sales and marketing. Right after starting their venture, they are hit with the huge problem of how to build their go-to-market organization and processes. It breaks the journey down into 9 steps, and explains why it is crucial not to skip steps in this journey in the rush to get ahead. The major emphasis of the course focuses on building a repeatable, scalable and profitable growth machine. Once you have that in place, you are ready to hit the gas and scale like crazy.
To see videos of the presentations, click here: https://www.forentrepreneurs.com/matrix-growth-academy-zero-to-100-videos/
Building a Sales and Marketing machine for a B2B software company involves many functions working together. This slide deck explores the process of funnel design that is highly buyer centric. It looks at the Buyer journey, and how to successfully construct a buying process that they buyer will enjoy going through, which is very different from the typical sale process that is designed from the vendors standpoint, and fails because the buyer is not motivated to go through the steps.
WebSummit 2018 - The SaaS Business Model & MetricsDavid Skok
SaaS businesses are extremely sensitive to a small number of important variables. If you are running a SaaS company, understanding how these variables drive your business model is crucial to long-term success. In this talk, David Skok, author of the now famous SaaS Metrics 2.0 blog post will talk through those key metrics and their impact on the overall SaaS business model.
Sales leader Derek Draper shares the Stage Management Guide that boosted his team's performance metrics across the board. Read the rest of the story on First Round Review: http://firstround.com/review/this-sales-plan-moves-the-needle-on-every-success-metric/
In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. The ability of making impression level bid decision and targeting to an individual user in real-time has fundamentally changed the landscape of the digital media. The further demand for automation, integration and optimisation in RTB brings new research opportunities in the IR fields, including information matching with economic constraints, CTR prediction, user behaviour targeting and profiling, personalised advertising, and attribution and evaluation methodologies. In this tutorial, teamed up with presenters from both the industry and academia, we aim to bring the insightful knowledge from the real-world systems, and to provide an overview of the fundamental mechanism and algorithms with the focus on the IR context. We will also introduce to IR researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.
Machine learning for profit: Computational advertising landscapeSharat Chikkerur
Online advertising is a multibillion-dollar industry than spans several subfields of computer science and engineering including information retrieval, machine learning, auction theory, optimization and distributed computing. Competitiveness in the marketplace is directly dependent on accuracy, scalability and sophistication of machine learning algorithms involved. The talk will present an overview of the computational advertising landscape, outline several key challenges, and discuss how machine learning addresses them.
http://www.buffalo.edu/cdse/CDSEdayslanding1/cdse-days2017/faculty_directory/SharatChikkerur.html
Spectrum auction Theory and Spectrum Price Modelwww.nbtc.go.th
Radio spectrum is scarce and invaluable telecommunication resource.
Reference :
http://www.ijird.com/index.php/ijird/article/view/84113/65018
Thanks for reading,
Noppadol Tiamnara
Computational Advertising in Yelp Local Adssoupsranjan
How does Yelp decide which relevant business or service to show you as an ad within 10s of milliseconds of your visit? What are the criteria and metrics by which we measure success of our ad serving system?
In this talk, the audience will learn about how Yelp figures out the best ad to show a user during his visit to Yelp: via a 2nd price auction amongst all the matching advertisers. Powering this 2nd price auction is a Machine Learning based system that predicts Click Through Rates (CTR) for all ads and an Auto-Bidding system that determines the optimal bid price for each ad per user request.
Yelp's local advertising presents challenges that are unique compared to display, social or mobile advertising. I'll motivate this via some trends and data observations. One of the interesting aspects is business categories and geolocation: How far are people willing to travel to visit a restaurant? What about professional services like plumbers: are users less or more sensitive to how far those are compared to restaurants?
I'll provide examples of how we use our open-sourced Map Reduce package (MRJob) to scale ML feature engineering and performance metric computation. I'll also provide details on our Machine Learning pipeline built using the popular open source packages: Python scikit-learn, Vowpal Wabbit and Apache Spark.
This talk would give you an in-depth overview of advertising systems, and why with increasingly sophisticated ad systems, in future we will wonder why we ever hated ads!
A theory of costly sequential bidding - Presentation SlidesDavid Hirshleifer
We propose a model of sequential bidding for a valuable object, such as a takeover target, when it is costly submit or revise a bid. An implication of the model is that bidding occurs in repeated jumps, a pattern that is consistent with certain types of natural auctions such as takeover contests. The jumps in bid communicate bidders' information rapidly, leading to contests that are completed with a small number of bids. The model provides several new results concerning revenue and efficiency relationships between different auctions, and provides an information-based interpretation of delays in bidding.
Prepublication version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=161013
Weinan Zhang's KDD15 Talk: Statistical Arbitrage Mining for Display AdvertisingJun Wang
We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.
pricing methods, perceived value pricing, competition based pricing, total cost based pricing or floor pricing, mark up pricing, target return pricing.
Lock-in and Entry Costs in Public ProcurementGRAPE
How to design auction environment in public procurement to balance the trade-off between facilitating free competition and minimizing switching costs?
Model of interaction between a buyer and potential sellers via
repeated low price auctions with bidder preference weights.
* improve entry rates without compromising switching cost motive: buyer
cooperatives, framework agreements, subsidized entry
* impact of variation in the set of potential bidders across auctions on
buyer’s welfare: sellers’ entry and exit, incomplete offer
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Learning, Prediction and Optimization in Real-Time Bidding based Display Advertising
1. Learning, Prediction and Optimisation in RTB
Display Advertising
Weinan Zhang, Shanghai Jiao Tong University
Jian Xu, TouchPal Inc.
http://www.optimalrtb.com/cikm16/
October 24, 2016, Indianapolis, United States
CIKM16 Tutorial
2. Speakers
• Weinan Zhang
– Assistant Professor at Shanghai Jiao Tong University
– Ph.D. from University College London 2016
– Machine learning, data mining in computational
advertising and recommender systems
• Jian Xu
– Principal Data Scientist at TouchPal, Mountain View
– Previous Senior Data Scientist and Senior Research
Engineer at Yahoo! US
– Data mining, machine learning, and computational
advertising
3. Tutorial Materials
• Web site:
http://www.optimalrtb.com/cikm16
• Supporting documents:
– RTB monograph
https://arxiv.org/abs/1610.03013
– RTB paper list:
https://github.com/wnzhang/rtb-papers
4. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
Weinan Zhang
90 min
Jian Xu
90 min
30 min break
5. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
14. Internet Advertising Frontier:
Real-Time Bidding (RTB) based Display Advertising
What is Real-Time Bidding?
• Every online ad view can be evaluated, bought,
and sold, all individually, and all instantaneously.
• Instead of buying keywords or a bundle of ad
views, advertisers are now buying users directly.
DSP/Exchange daily traffic
Advertising iPinYou, China 18 billion impressions
YOYI, China 5 billion impressions
Fikisu, US 32 billon impressions
Finance New York Stock Exchange 12 billion shares
Shanghai Stock Exchange 14 billion shares
Query per second
Turn DSP 1.6 million
Google 40,000 search
[Shen, Jianqiang, et al. "From 0.5 Million to 2.5 Million: Efficiently Scaling up Real-Time Bidding." Data Mining (ICDM),
2015 IEEE International Conference on. IEEE, 2015.]
15. Suppose a student regularly reads articles on emarketer.com
Content-related ads
19. RTB Display Advertising Mechanism
• Buying ads via real-time bidding (RTB), 10B per day
RTB
Ad
Exchange
Demand-Side
Platform
Advertiser
Data
Management
Platform
0. Ad Request
1. Bid Request
(user, page, context)
2. Bid Response
(ad, bid price)
3. Ad Auction
4. Win Notice
(charged price)
5. Ad
(with tracking)
6. User Feedback
(click, conversion)
User Information
User Demography:
Male, 26, Student
User Segmentations:
London, travelling
Page
User
<100 ms
20. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
22. Modeling
• n bidders
• Each bidder i has value vi for the item
– “willingness to pay”
– Known only to him – “private value”
• If bidder i wins and pays pi, his utility is vi – pi
– In addition, the utility is 0 when the bidder loses.
• Note: bidders prefer losing than paying more than their
value.
23. Strategy
• A strategy for each bidder
– how to bid given your intrinsic, private value?
– a strategy here is a function, a plan for the game.
Not just a bid.
• Examples for strategies:
– bi(vi) = vi (truthful)
– bi(vi) = vi /2
– bi(vi) = vi /n
– If v<50, bi(vi) = vi
otherwise, bi(vi) = vi +17
• Can be modeled as normal form game, where these
strategies are the pure strategies.
• Example for a game with incomplete information.
B(v)=v B(v)=v
/2
B(v)=v
/n
….
B(v)=v
…
24. Strategies and equilibrium
• An equilibrium in the auction is a profile of
strategies B1,B2,…,Bn such that:
– Dominant strategy equilibrium: each strategy is optimal
whatever the other strategies are.
– Nash equilibrium: each strategy is a best response to the
other strategies.
B(v)=v B(v)=v/2 B(v)=v/n ….
B(v)=v
…
25. Bayes-Nash equilibrium
• Recall a set of bidding strategies is a Nash
equilibrium if each bidder’s strategy
maximizes his payoff given the optimal
strategies of the others.
– In auctions: bidders do not know their opponent’s
values, i.e., there is incomplete information.
– Each bidder’s strategy must maximize her
expected payoff accounting for the uncertainty
about opponent values.
27. Equilibrium in 1st-price auctions
• Suppose bidder i’s value is vi in [0,1], which is only
known by bidder i.
• Given this value, bidder i must submit a sealed bid
bi (vi )
• We view bidder i’s strategy as a bidding function bi :
[0,1] -> R+. Some properties:
– Bidders with higher values will place higher bids. So bi is a
strictly increasing function
– Bidders are also symmetric. So bidders with the same
value will submit the same bid: bi = b (symmetric Nash
equilibrium)
– Win(bi) = F(vi), where F is the C.D.F. of the true value
distribution
28. Equilibrium in 1st-price auctions
• Bidder 1’s payoff
• The expected payoff of bidding b1 is given by
• An optimal strategy bi should maximize
v1 - b1 if b1 > max{b(v2 ),...,b(vn )}
0 if b1 £ max{b(v2 ),...,b(vn )}
ì
í
ï
îï
p(b1) = (v1 - b1)P(b1 > max{b(v2 ),...,b(vn )
= (v1 - b1)P(b1 > b(v2 ),...,b1 > (vn ))
p(b1)
})
29. Equilibrium in 1st-price auctions
• Suppose that bidder i cannot attend the auction and
that she asks a friend to bid for her
– The friend knows the equilibrium bidding function b* but
doe not know vi
– Bidder tells his friend the value as x and wants him to
submit the bid b* (x)
– The expected pay off in this case is
• The expected payoff is maximized when reporting
his true value vi to his friend (x = vi)
p(b*
,x) = (v1 - b*
(x))P(b*
(x) > b*
(v2 ),...,b*
(x) > b*
(vn ))
= (v1 - b*
(x))P(x > v2,...,x > vn ) = (v1 - b*
(x))FN-1
(x)
30. Equilibrium in 1st-price auctions
• So if we differentiate the expected payoff with
respect to x, the resulting derivative must be zero
when x = vi :
• The above equals zero when x = vi ; rearranging
yields:
dp(b*
,x)
dx
=
d(v1 - b*
(x))FN-1
(x)
dx
= (N -1)FN-2
(x) f (x)(v1 - b*
(x))- FN-1
(x)b*
' (x)
(N -1)FN-2
(v1) f (v1)v1
= FN-1
(v1)b*
' (v1)+ (N -1)FN-2
(v1) f (v1)b*
(v1)
=
dFN-1
(v1)b*
(v1)
dv
31. Equilibrium in 1st-price auctions
• Taking the integration on both side
• If we assume a bidder with value zero must bid zero,
the above constant is zero. Therefore, we have
(replace vi with v)
• It shows that in the equilibrium, each bidder bids
the expectation of the second-highest bidder’s value
conditional on winning the auction.
32. Untruthful bidding in 1st-price auctions
• Suppose that each bidder’s value is uniformly
distributed on [0,1].
– Replacing F(v)=v and f(v)=1 gives
33. Equilibrium in 2nd-price auctions
• bidder 1’s payoff
• The expected payoff of bidding b1 is given by
• Suppose b1 < v1, if b1 is increased to v1 the integral
increases by the amount
• The reverse happens if b1 > v1
v1 - bi if b1 > bi > max{b(v2 ),...,b(vi-1),b(vi+1),...,b(vn )}
0 if b1 £ max{b(v2 ),...,b(vn )}
ì
í
ï
îï
34. Equilibrium in 2nd-price auctions
• bidder 1’s payoff
• The expected payoff of bidding b1 is given by
• Or taking derivative of π(v1, b1) w.r.t. b1 yields b1 = v1
v1 - bi if b1 > bi > max{b(v2 ),...,b(vi-1),b(vi+1),...,b(vn )}
0 if b1 £ max{b(v2 ),...,b(vn )}
ì
í
ï
îï
So telling the truth b1 = v1 is a Bayesian
Nash equilibrium bidding strategy!
35. Reserve Prices and Entry Fees
• Reserve Prices: the seller is assumed to have
committed to not selling below the reserve
– Reserve prices are assumed to be known to all bidders
– The reserve prices = the minimum bids
• Entry Fees: those bidders who enter have to pay
the entry fee to the seller
• They reduce bidders’ incentives to participate,
but they might increase revenue as
– 1) the seller collects extra revenues
– 2) bidders might bid more aggressively
36. RTB Auctions
• Second price auction with reserve price
• From a bidder’s perspective, the market price
z refers to the highest bid from competitors
• Payoff: (vimpression– z) × P(win)
• Value of impression depends on user response
37. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
38. RTB Display Advertising Mechanism
• Buying ads via real-time bidding (RTB), 10B per day
RTB
Ad
Exchange
Demand-Side
Platform
Advertiser
Data
Management
Platform
0. Ad Request
1. Bid Request
(user, page, context)
2. Bid Response
(ad, bid price)
3. Ad Auction
4. Win Notice
(charged price)
5. Ad
(with tracking)
6. User Feedback
(click, conversion)
User Information
User Demography:
Male, 26, Student
User Segmentations:
London, travelling
Page
User
<100 ms
40. User response estimation problem
• Click-through rate estimation as an example
• Date: 20160320
• Hour: 14
• Weekday: 7
• IP: 119.163.222.*
• Region: England
• City: London
• Country: UK
• Ad Exchange: Google
• Domain: yahoo.co.uk
• URL: http://www.yahoo.co.uk/abc/xyz.html
• OS: Windows
• Browser: Chrome
• Ad size: 300*250
• Ad ID: a1890
• User tags: Sports, Electronics
Click (1) or not (0)?
Predicted CTR (0.15)
41. Feature Representation
• Binary one-hot encoding of categorical data
x=[Weekday=Wednesday, Gender=Male, City=London]
x=[0,0,1,0,0,0,0 0,1 0,0,1,0…0]
High dimensional sparse binary feature vector
42. Linear Models
• Logistic Regression
– With SGD learning
– Sparse solution
• Online Bayesian Probit Regression
43. ML Framework of CTR Estimation
• A binary regression problem
– Large binary feature space (>10 millions)
• Bloom filter to detect and add new features (e.g., > 5 instances)
– Large data instance number (>10 millions daily)
– A seriously unbalanced label
• Normally, #click/#non-click = 0.3%
• Negative down sampling
• Calibration
– An isotonic mapping from prediction to calibrated prediction
44. Logistic Regression
• Prediction
• Cross Entropy Loss
• Stochastic Gradient Descent Learning
[Lee et al. Estimating Conversion Rate in Display Advertising from Past Performance Data. KDD 12]
45. Logistic Regression with SGD
• Pros
– Standardised, easily understood and implemented
– Easy to be parallelised
• Cons
– Learning rate η initialisation
– Uniform learning rate against different binary
features
46. Logistic Regression with FTRL
• In practice, we need a sparse solution as >10 million feature dimensions
• Follow-The-Regularised-Leader (FTRL) online Learning
[McMahan et al. Ad Click Prediction : a View from the Trenches. KDD 13]
s.t.
• Online closed-form update of FTRL
t: current example index
gs: gradient for example t
adaptively selects regularisation functions
[Xiao, Lin. "Dual averaging method for regularized stochastic learning and online optimization." Advances in Neural Information Processing Systems. 2009]
47. Online Bayesian Probit Regression
to approximate message passing. In order
full factorial structure of the likelihood,
wo latent variables , and consider the
density function which
(5)
n can be understood in terms of the
ative process, which is also reflected in
in Figure 1.
Sample weights from the Gaussian
alculate the score for x as the inner
, such that .
dd zero-mean Gaussian noise to obtain
h that .
etermine by a threshold on the noisy
ro, such that .
skills in TrueSkill after a hypothetical m
team with known skill of zero. Given the
Figure 1 together with Table 1 in the a
update equations can be derived.
3.3.1. UPDATE EQUATIONS FOR ONLINE
The update equations represent a mappin
posterior parameter values based o
pairs ̃ ̃ . In terms of
calculation can viewed as following the m
schedule towards the weights . We d
variance for a given input as
The update for the posterior parameters is
̃ (
̃ * (
48. Linear Prediction Models
• Pros
– Highly efficient and scalable
– Explore larger feature space and training data
• Cons
– Modelling limit: feature independence assumption
– Cannot capture feature interactions unless defining
high order combination features
• E.g., hour=10AM & city=London & browser=Chrome
50. Factorisation Machines
• Prediction based on feature embedding
– Explicitly model feature interactions
• Second order, third order etc.
– Empirically better than logistic regression
– A new way for user profiling
[Oentaryo et al. Predicting response in mobile advertising with hierarchical importance-
aware factorization machine. WSDM 14]
[Rendle. Factorization machines. ICDM 2010.]
Logistic Regression Feature Interactions
51. Factorisation Machines
• Prediction based on feature embedding
[Oentaryo et al. Predicting response in mobile advertising with hierarchical importance-
aware factorization machine. WSDM 14]
[Rendle. Factorization machines. ICDM 2010.]
Logistic Regression Feature Interactions
For x=[Weekday=Friday, Gender=Male, City=Shanghai]
52. • Feature embedding for another field
Field-aware Factorisation Machines
[Juan et al. Field-aware Factorization Machines for CTR Prediction. RecSys 2016.]
Field-aware field embedding
For x=[Weekday=Friday, Gender=Male, City=Shanghai]
53. Gradient Boosting Decision Trees
• Additive decision trees for prediction
• Each decision tree
[Chen and He. Higgs Boson Discovery with Boosted Trees . HEPML 2014.]
54. Gradient Boosting Decision Trees
• Learning
[Chen and He. Higgs Boson Discovery with Boosted Trees . HEPML 2014.]
[Tianqi Chen. https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf]
55. Combined Models: GBDT + LR
[He et al. Practical Lessons from Predicting Clicks on Ads at Facebook . ADKDD 2014.]
56. Combined Models: GBDT + FM
[http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf]
57. Neural Network Models
• Difficulty:
Impossible to
directly deploy
neural network
models on such
data
1M
500
500M
E.g., input features 1M, first layer 500, then 500M parameters for first layer
58. Review Factorisation Machines
• Prediction based on feature embedding
– Embed features into a k-dimensional latent space
– Explore the feature interaction patterns using vector inner-
product
[Oentaryo et al. Predicting response in mobile advertising with hierarchical importance-
aware factorization machine. WSDM 14]
[Rendle. Factorization machines. ICDM 2010.]
Logistic Regression Feature Interactions
60. [Zhang et al. Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction. ECIR 16]
[Factorisation Machine Initialised]
Factorisation-machine supported Neural
Networks (FNN)
61. [Zhang et al. Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction. ECIR 16]
Factorisation-machine supported Neural
Networks (FNN)
• Chain rule to update factorisation machine parameters
63. Product Operations as Feature
Interactions
[Yanru Qu et al. Product-based Neural Networks for User Response Prediction. ICDM 2016]
64. Product-based Neural Networks (PNN)
Inner Product
Or
Outer Product
[Yanru Qu et al. Product-based Neural Networks for User Response Prediction. ICDM 2016]
65. Convolutional Click Prediction Model (CCPM)
• CNN to (partially) select good feature combinations
[Qiang Liu et al. A convolutional click prediction model. CIKM 2015]
67. Training with Instance Bias
[Zhang et al. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. KDD 2016.]
68. Unbiased Learning
• General machine learning problem
• But the training data distribution is q(x)
– A straightforward solution: importance sampling
[Zhang et al. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. KDD 2016.]
69. Unbiased CTR Estimator Learning
[Zhang et al. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. KDD 2016.]
70. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
71. RTB Display Advertising Mechanism
• Buying ads via real-time bidding (RTB), 10B per day
RTB
Ad
Exchange
Demand-Side
Platform
Advertiser
Data
Management
Platform
0. Ad Request
1. Bid Request
(user, page, context)
2. Bid Response
(ad, bid price)
3. Ad Auction
4. Win Notice
(charged price)
5. Ad
(with tracking)
6. User Feedback
(click, conversion)
User Information
User Demography:
Male, 26, Student
User Segmentations:
London, travelling
Page
User
<100 ms
72. Data of Learning to Bid
– Bid request features: High dimensional sparse binary vector
– Bid: Non-negative real or integer value
– Win: Boolean
– Cost: Non-negative real or integer value
– Feedback: Binary
• Data
73. Problem Definition of Learning to Bid
• How much to bid for each bid request?
– Find an optimal bidding function b(x)
• Bid to optimise the KPI with budget constraint
Bid Request
(user, ad, page, context)
Bid Price
Bidding
Strategy
77. Bid Landscape Forecasting
• Log-Normal Distribution
Auction
Winning
Probability
[Cui et al. Bid Landscape Forecasting in Online Ad Exchange Marketplace. KDD 11]
78. Bid Landscape Forecasting
• Price Prediction via Linear Regression
– Modelling censored data in lost bid requests
[Wu et al. Predicting Winning Price in Real Time Bidding with Censored Data. KDD 15]
79. Survival Tree Models
[Yuchen Wang et al. Functional Bid Landscape Forecasting for Display Advertising. ECMLPKDD 2016 ]
Node split
Based on
Clustering
categories
80. Bidding Strategies
• How much to bid for each bid request?
• Bid to optimise the KPI with budget constraint
Bid Request
(user, ad, page, context)
Bid Price
Bidding
Strategy
81. Classic Second Price Auctions
• Single item, second price (i.e. pay market price)
Reward given a bid:
Optimal bid:
Bid true value
82. Truth-telling Bidding Strategies
• Truthful bidding in second-price auction
– Bid the true value of the impression
– Impression true value =
– Averaged impression value = value of click * CTR
– Truth-telling bidding:
[Chen et al. Real-time bidding algorithms for performance-based display ad allocation. KDD 11]
Value of click, if clicked
0, if not clicked
83. Truth-telling Bidding Strategies
• Pros
– Theoretic soundness
– Easy implementation (very widely used)
• Cons
– Not considering the constraints of
• Campaign lifetime auction volume
• Campaign budget
– Case 1: $1000 budget, 1 auction
– Case 2: $1 budget, 1000 auctions
[Chen et al. Real-time bidding algorithms for performance-based display ad allocation. KDD 11]
84. Non-truthful Linear Bidding
• Non-truthful linear bidding
– Tune base_bid parameter to maximise KPI
– Bid landscape, campaign volume and budget
indirectly considered
[Perlich et al. Bid Optimizing and Inventory Scoring in Targeted Online Advertising. KDD 12]
85. ORTB Bidding Strategies
• Direct functional optimisation
CTRwinning function
bidding function
budget
Est. volume cost upperbound
[Zhang et al. Optimal real-time bidding for display advertising. KDD 14]
• Solution: Calculus of variations
86. Optimal Bidding Strategy Solution
86
[Zhang et al. Optimal real-time bidding for display advertising. KDD 14]
87. Unbiased Optimisation
• Bid optimization on ‘true’ distribution
• Unbiased bid optimization on biased distribution
[Zhang et al. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. KDD 2016.]
88. Unbiased Bid Optimisation
A/B Testing
on Yahoo!
DSP.
[Zhang et al. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. KDD 2016.]
92. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
93. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
94. Conversion Attribution
• Assign credit% to each channel according to contribution
• Current industrial solution: last-touch attribution
[Shao et al. Data-driven multi-touch attribution models. KDD 11]
Ad on Yahoo Sports Ad on Facebook Ad on Amazon
Ad on Google
Ad on TV
96. A Good Attribution Model
• Fairness
– Reward an individual channel in accordance with
its ability to affect the likelihood of conversion
• Data driven
– It should be built based on ad touch and
conversion data of a campaign
• Interpretability
– Generally accepted by all the parties
[Dalessandro et al. Casually Motivated Attribution for Online Advertising. ADKDD 11]
97. Bagged Logistic Regression
• For M iterations
– Sample 50% data instances and 50% features
– Train a logistic regression model and record the feature
weights
• Average the weights of a feature
Display Search Mobile Email Social Convert?
1 1 0 0 1 1
1 0 1 1 1 0
0 1 0 1 0 1
0 0 1 1 1 0
[Shao et al. Data-driven multi-touch attribution models. KDD 11]
98. A Probabilistic Attribution Model
• Conditional probabilities
• Attributed contribution (not-normalized)
[Shao et al. Data-driven multi-touch attribution models. KDD 11]
99. [Shao et al. Data-driven multi-touch attribution models. KDD 11]
100. [Shao et al. Data-driven multi-touch attribution models. KDD 11]
101. Data-Driven Probabilistic Models
[Shao et al. Data-driven multi-touch attribution models. KDD 11]
• A more generalized and data-driven model
[Dalessandro et al. Causally Motivated Attribution for Online Advertising. ADKDD 11]
– is the probability that the ad touch sequence begins
with
• The “relatively heuristic” data-driven model
102. Attribution Comparison: LTA vs MTA
[Dalessandro et al. Casually Motivated Attribution for Online Advertising. ADKDD 11]
103. Shapley Value based Attribution
• Coalitional game
– How much does a player contribute in the game?
[Fig source: https://pjdelta.wordpress.com/2014/08/10/group-project-how-much-did-i-contribute/]
104. Shapley Value based Attribution
• Coalitional game
– is the conversion rate of different subset of publishers
– The Shapley value of publisher is
[Berman, Ron. Beyond the last touch: Attribution in online advertising.” Available at SSRN 2384211 (2013)]
CVR of those touched by all
the publishers in
105. Survival theory-based model
• Use addictive hazard functions to explicitly model:
– the strength of influence, and
– the time-decay of the influence
[Zhang et al. Multi-Touch Attribution in Online Advertising with Survival Theory. ICDM 2014]
106. • Establish a graph from observed user journeys
Markov graph-based approach
[Anderl et al. Mapping the customer journey: A graph-based framework for online attribution modeling. SSRN 2014]
107. • Attribute based on probability change of reaching
conversion state
Markov graph-based approach
[Anderl et al. Mapping the customer journey: A graph-based framework for online attribution modeling. SSRN 2014]
108. MTA-based budget allocation
• Typical advertiser
hierarchy
• Typical budget
allocation scheme
[Geyik et al. Multi-Touch Attribution Based Budget Allocation in Online Advertising. ADKDD 14]
109. • Estimate sub-campaign spending capability
– New sub-campaign: assign a learning budget
– Existing sub-campaign: assign an x% more budget
• Calculate ROI of each sub-campaign
• Allocate budget in a
cascade fashion
1 if is the last touch
point else 0 (LTA)
(MTA)
MTA-based budget allocation
[Geyik et al. Multi-Touch Attribution Based Budget Allocation in Online Advertising. ADKDD 14]
110. MTA-based budget allocation
• Results on a real ad campaign
[Geyik et al. Multi-Touch Attribution Based Budget Allocation in Online Advertising. ADKDD 14]
111. Attribution and Bidding
• For CPA campaigns, conventional bidding strategy is
to bid prop. to estimated action rate (a.k.a.
conversion rate). Is that always correct?
[Xu et al. Lift-Based Bidding in Ad Selection. AAAI 2016.]
113. Rational DSPs for CPA advertisers
• DSP’s perspective:
– Cost: second price in the auction
– Reward: CPA if (1) there is action, and (2) the
action is attributed to it
– A rational DSP will always bid
In LTA, p(attribution|action) is always 1 for the last
toucher. Therefore DSPs are bidding to maximize their
chance to be attributed instead of maximizing conversions.
114. Bidding in Multi-Touch Attribution
• Current bidding strategy (driven by LTA)
• A new bidding strategy (driven by MTA)
– If attribution is based on the AR lift
[Xu et al. Lift-Based Bidding in Ad Selection. AAAI 2016.]
Lift- based bidding
115. Lift-based bidding
• Estimating action rate lift
– Learn a generic action prediction model on top
of features extracted from user-states
– Then action rate lift can be estimated by
• Deriving the base_bid
[Xu et al. Lift-Based Bidding in Ad Selection. AAAI 2016.]
117. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
118. Pacing Control
• Budget pacing control helps advertisers to define
and execute how their budget is spent over the
time.
• Why?
– Avoid premature campaign stop, overspending and
spending fluctuations.
– Reach a wider range of audience
– Build synergy with other marketing campaigns
– Optimize campaign performance
119. Examples
[Lee et al. Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising. ADKDD 13]
120. Two streams of approaches
Bid modification Probabilistic throttling
[Xu et al. Smart Pacing for Effective Online Ad Campaign Optimization. KDD 2015.]
121. Bid modification with PID controller
• Add a monitor, a controller and an actuator module into the
bidding system
• Achieve reference KPI (e.g. eCPC) by bid modification
[Zhang et al. Feedback Control of Real-Time Display Advertising. WSDM 2016.]
122. Bid modification with PID controller
• Current control signal is calculated by PID controller
• Bid price is adjusted by taking into account current control signal
• A baseline controller: Water-level controller
[Zhang et al. Feedback Control of Real-Time Display Advertising. WSDM 2016.]
The control signal
Reference KPI Actual KPI value
123.
124. • Online eCPC control performance of a mobile game
campaign
Bid modification with PID controller
[Zhang et al. Feedback Control of Real-Time Display Advertising. WSDM 2016.]
125. Probabilistic throttling with
conventional feedback controller
• P(t): pacing-rate at time slot t
• Leverage a conventional feedback controller:
– P(t)=P(t–1)*(1–R) if budget spent > allocation
– P(t)=P(t–1)*(1+R) if budget spent < allocation
[Agarwal et al. Budget Pacing for Targeted Online Advertisements at LinkedIn. KDD 2014.]
126. Probabilistic throttling with adaptive
controller
• Leverage an adaptive controller
is the desired spend (allocated) at time slot t+1. Different
desired spending patterns can incur different calculation.
[Lee et al. Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising. ADKDD 13]
Desired spending in the
next time-slot
Forecasted request volume and
bid win rate in the next time-slot
127. Pacing control for campaign
optimization
• Campaign optimization objectives:
– Reach delivery and performance goals
• Branding campaigns: Spend out budget > Campaign
performance (e.g., in terms of eCPC or eCPA)
• Performance campaigns: Meet performance goal >
Spend as much budget as possible.
– Execute the budget pacing plan
– Reduce creative serving cost
Can we achieve all these objectives by pacing control?
[Xu et al. Smart Pacing for Effective Online Ad Campaign Optimization. KDD 2015.]
128. Smart pacing
1.0
0.6
1.0
0.1
0.001
0.001
0.001
0.001
1.0
1.0
0.8 1.0
0.001 0.2
Layer 3
Layer 2
Layer 1
Layer 0
Ad request
volume
Time slot
Budget pacing plan
Actual
spending
Time slot
High responding
Low responding
0.001 0.001
Slow down Speed up
[Xu et al. Smart Pacing for Effective Online Ad Campaign Optimization. KDD 2015.]
132. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
133. Does targeting help online advertising?
• Segment user based on …
– LP: Long-term Page-view , SP: Short-term Page-view
– LQ: Long-term Query , SQ: Short-term Query
[J Yan, et al. How much can behavioral targeting help online advertising? WWW 2009]
Compare the best CTR segment with baseline (random users)
134. User segmentation
• Different user segmentation algorithms may have different
results
[J Yan, et al. How much can behavioral targeting help online advertising? WWW 2009]
135. User segmentation
• From user – documents to user – topics
– Topic modeling using PLSA, LDA, etc.
[X Wu et al. Probabilistic latent semantic user segmentation for behavioral targeted advertising.
Intelligence for Advertising 2009]
User Topic Term
136. Targeting landscape
• Targeting: reach the precise users who are receptive to
the marketing messages.
Geo-targeting Demo-targeting
Behavioral
Targeting
Search Re-
targeting
Mail Re-
targeting
Social Targeting
Site Re-targeting
Desired users
Web-site targeting
Proximity
Targeting
137. Targeting landscape
• A bit too complicated …
domain1,
domain2,
Purchase CAT1,
Purchase CAT2,
… MRT
keyword1,
keyword2,
… SRT
Facebook “Like”1,
Facebook “Like”2,
…
Social
Bazooka CAT1,
Bazooka CAT2,
…
BT
Audience
Match
Digital
Direct
Proximity
Geo Demo Device
Advertiser
(ad campaign)
etc.
138. Audience expansion
• AEX Simplifies targeting by discovering similar
(prospective) customers
[J Shen, et al., Effective Audience Extension in Online Advertising, KDD 2015]
139. Rule mining-based approach
• Identify feature-pair-based associative
classification rules
– Affinity that a feature-pair towards conversion:
– Top k feature (pairs) are kept as scoring rules
Especially good for those tail campaigns (e.g. CVR < 0.01%)
[Mangalampalli et al, A feature-pair-based associative classification approach to look-alike
modeling for conversion-oriented user-targeting in tail campaigns. WWW 2011]
Probability to observe
feature-pair f in data
140. Rule mining-based approach
• Campaign C1: a tail campaign
• Campaign C2: a head campaign
[Mangalampalli et al, A feature-pair-based associative classification approach to look-alike
modeling for conversion-oriented user-targeting in tail campaigns. WWW 2011]
144. Audience Expansion for OSN Advertising
• Campaign-agnostic: enrich member profile attributes
• Campaign-aware: identify similar members
[H Liu et al. Audience expansion for online social network advertising. KDD 2016]
145. Audience Expansion for OSN Advertising
• Member similarity
evaluation
– Density of a
segment:
– Expansion ratio vs
Density ratio
[H Liu et al. Audience expansion for online social network advertising. KDD 2016]
146. Transferred lookalike
• Web browsing prediction (CF task)
• Ad response prediction (CTR task)
[Zhang et al. Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to
CTR Estimation. ECIR 2016]
user feature publisher feature K-dimensional latent vector
ad feature
147. Transferred lookalike
Using web browsing data, which is largely available, to infer the ad clicks
[Zhang et al. Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to
CTR Estimation. ECIR 2016]
148. Joint Learning in Transferred lookalike
[Zhang et al. Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to
CTR Estimation. ECIR 2016]
149. Table of contents
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
150. Reserve price optimisation
The task:
• To find the optimal reserve prices to maximize publisher revenue
The challenge:
• Practical constraints v.s theoretical assumptions
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
151. Why
• Suppose it is second price auction and 𝑏1, 𝑏2
are first and second prices
– Preferable case: 𝑏1 ≥ 𝛼 > 𝑏2 (increases revenue)
– Undesirable case: 𝛼 > 𝑏1 (lose revenue)
152. • Suppose: two bidders, whose private values 𝑏1, 𝑏2 are both
drawn from Uniform[0, 1]
• Without a reserve price, the expected payoff 𝑟 is:
• With α = 0.2:
• With α = 0.5:
• With α = 0.6:
An example
[Ostrovsky et al, Reserve prices in internet advertising auctions: A field experiment. EC 2011]
𝑟 = 𝐸 min 𝑏1, 𝑏2 = 0.33
𝑟 = 𝐸 min 𝑏1, 𝑏2 𝑏1 > 0.5, 𝑏2 > 0.5 + (0.5 × 0.5) × 2 × 0.5 = 0.42
𝑟 = 𝐸 min 𝑏1, 𝑏2 𝑏1 > 0.2, 𝑏2 > 0.2 + (0.8 × 0.2) × 2 × 0.2 = 0.36
𝑟 = 𝐸 min 𝑏1, 𝑏2 𝑏1 > 0.6, 𝑏2 > 0.6 + 0.6 × 0.4 × 2 × 0.6 = 0.405
Paying the second highest price Paying the reserve price
153. Theoretically optimal reserve price
• In the second price auctions, an advertiser bid its private
value 𝑏
• Suppose bidders are risk-neutral and symmetric (i.e. having
same distributions) with bid C.D.F 𝐹 𝑏
• The publisher also has a private value 𝑉𝑝
• The optimal reserve price is given by:
[Levin and Smith, Optimal Reservation Prices in Auctions, 1996]
𝛼 =
1 − 𝐹 𝛼
𝐹′ 𝛼
+ 𝑉𝑝
154. Results from a field experiment
• Using the theoretically optimal reserve price on Yahoo!
Sponsored search
Mixed results
[Ostrovsky et al, Reserve prices in internet advertising auctions: A field experiment. EC 2011]
155. • Advertisers have their own bidding strategies (No access
to publishers)
• They change their strategies frequently
Bidding strategy is a mystery
Many advertisers bid at fixed values
with bursts and randomness.
And they come and go
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
156. Uniform/Log-normal distributions do NOT fit well
Test at the placement level
(because we usually set reserve prices
on placements)
Test at the auction level
• Chi-squared test for Uniformity
• Anderson-Darling test for Normality
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
157. A simplified dynamic game
• Players: auction winner ,publisher
• Initial status: : ; otherwise
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
158. OneShot: the algorithm based on
dominant strategy
• The algorithm essentially uses a conventional
feedback controller
• A practical example setting of the parameters:
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
159. OneShot performance
[Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
160. [Yuan et al. An Empirical Study of Reserve Price Optimisation in Display Advertising. KDD 2014]
Advertiser attrition concern
161. Optimal reserve price in upstream
auctions
• A different problem
setting
– Upstream charges a
revenue-share (e.g.
25%) from each
winning bid.
– What is the optimal
reserve price for such
a marketplace?
[Alcobendas et al., Optimal reserve price in upstream auctions: Empirical application on
online video advertising. KDD 2016]
162. Optimal reserve price in upstream
auctions
• Assume bidder’s valuation of the inventory is an i.i.d. realization of the
random variable V, and bidders are risk neutral, the optimal reserve
price for upstream marketplace satisfies
If without downstream auction, optimal condition is
Probability of winning downstream auction
Probability that a bidder wins
the upstream auction with bid u
Expected price if having at least
one bidder above reserve price
Support interval of V
163. Optimal reserve price in upstream
auctions
[Alcobendas et al., Optimal reserve price in upstream auctions: Empirical application on
online video advertising. KDD 2016]
164. Thank You
• RTB system
• Auction mechanisms
• User response estimation
• Learning to bid
• Conversion attribution
• Pacing control
• Targeting and audience expansion
• Reserve price optimization
Learning, Prediction and Optimisation in RTB
Display Advertising
Weinan Zhang
(wnzhang AT sjtu.edu.cn)
Jian Xu
(jian.xu AT cootek.cn)
CIKM16 Tutorial