Computational Advertising has recently emerged as a new scientific sub-discipline, bridging the gap among the areas such as information retrieval, data mining, machine learning, economics, and game theory. In this tutorial, I shall present a number of challenging issues by analogy with financial markets. The key vision is that display opportunities are regarded as raw material “commodities” similar to petroleum and natural gas – for a particular ad campaign, the effectiveness (quality) of a display opportunity shouldn’t rely on where it is brought and whom it belongs, but it should depend on how good it will benefit the campaign (e.g., the underlying web users’ satisfactions or respond rates). With this vision in mind, I will go through the recently emerged real-time advertising, aka Real-Time Bidding (RTB), and provide the first empirical study of RTB on an operational ad exchange. We show that RTB, though suffering its own issue, has the potential of facilitating a unified and interconnected ad marketplace, making it one step closer to the properties in financial markets. At the latter part of this talk, I will talk about Programmatic Premium, i.e., a counterpart to RTB to make display opportunities in future time accessible. For that, I will present a new type of ad contracts, ad options, which have the right, but no obligation to purchase ads. With the option contracts, advertisers have increased certainty about their campaign costs, while publishers could raise the advertisers’ loyalty. I show that our proposed pricing model for the ad option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines.
RTB tutorial Version 2.
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. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. However, despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for many reasons. In this tutorial, together with invited distinguished speakers from online advertising industry, we aim to bring the insightful knowledge from the real-world systems to bridge the gaps and provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of this new frontier of computational advertising. We will also introduce to researchers the datasets, tools, and platforms which are publicly available thus they can get hands-on quickly.
This tutorial aims to provide not only a comprehensive and systematic 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 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.
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.
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.
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.
RTB tutorial Version 2.
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. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. However, despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for many reasons. In this tutorial, together with invited distinguished speakers from online advertising industry, we aim to bring the insightful knowledge from the real-world systems to bridge the gaps and provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of this new frontier of computational advertising. We will also introduce to researchers the datasets, tools, and platforms which are publicly available thus they can get hands-on quickly.
This tutorial aims to provide not only a comprehensive and systematic 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 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.
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.
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.
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.
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
1. Second price is cheated
2. Cookie lifetime is short
3. Major advertisers integrate to RTB ecosystem directly
4. Targeting is getting more sophisticated
5. RTB ecosystem is highly fragmented
6. Fraud is a HUGE problem
Paul Gill, Head of RTB, presented on what Real-Time Bidding is and how to implement a successful campaign at our Real-Time Bidding Best Practice Seminar.
The Social Media Scoreboard 2014: Understanding the Value and Impact of Fan E...WassermanMediaGroup
What is the value of social media? That is a question everyone seeks to answer. If selected to participate in SXsports 2015, this panel will provide attendees with a look into the ways that sports fans are using social media, how that use differs from non-fans, and how those insights can be used to more effectively engage with and target fans moving forward. Panelists will utilize research from The Social Media Scoreboard, a comprehensive and insightful report on social media and its influence on professional sports from Navigate Research and Wasserman Media Group, to help attendees understand the value of fan engagement and social media assets within sports sponsorship. Panelists will then discuss practical, best-in-class examples that have utilized this research to effectively optimize social media for fan engagement.
Why Online Advertising?
With the advancement of internet , web has become the most preferred medium for the businesses to promote their brands and services.
Traditional advertising involved putting up hoardings , billboards , distributing pamphlet hoping that the customers would view them. But there wasn't any way to measure the success of the advertisement.
To Know more about online advertising, visit: http://www.knowonlineadvertising.com
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.
Internet advertising an interplay among advertisers,online publishers, ad exc...Trieu Nguyen
Internet Advertising An Interplay among Advertisers,Online Publishers, Ad Exchanges and Web Users, Computational Advertising, contextual advertising, sponsored search, user behaviour targeting
Data-driven Reserve Prices for Social Advertising Auctions at LinkedInKun Liu
Online advertising auctions constitute an important source of revenue for search engines such as Google and Bing, as well as social networks such as Facebook, LinkedIn and Twitter. We study the problem of setting the optimal reserve price in a Generalized Second Price auction, guided by auction theory with suitable adaptations to social advertising at LinkedIn. Two types of reserve prices are deployed: one at the user level, which is kept private by the publisher, and the other at the audience segment level, which is made public to advertisers. We demonstrate through field experiments the effectiveness of this reserve price mechanism to promote demand growth, increase ads revenue, and improve advertiser experience.
What is Paid Search Advertising?
Paid search advertising with Google Ads is among the most effective channels in modern marketing. However, you can only reap the rewards when you understand what you are doing.
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
1. Second price is cheated
2. Cookie lifetime is short
3. Major advertisers integrate to RTB ecosystem directly
4. Targeting is getting more sophisticated
5. RTB ecosystem is highly fragmented
6. Fraud is a HUGE problem
Paul Gill, Head of RTB, presented on what Real-Time Bidding is and how to implement a successful campaign at our Real-Time Bidding Best Practice Seminar.
The Social Media Scoreboard 2014: Understanding the Value and Impact of Fan E...WassermanMediaGroup
What is the value of social media? That is a question everyone seeks to answer. If selected to participate in SXsports 2015, this panel will provide attendees with a look into the ways that sports fans are using social media, how that use differs from non-fans, and how those insights can be used to more effectively engage with and target fans moving forward. Panelists will utilize research from The Social Media Scoreboard, a comprehensive and insightful report on social media and its influence on professional sports from Navigate Research and Wasserman Media Group, to help attendees understand the value of fan engagement and social media assets within sports sponsorship. Panelists will then discuss practical, best-in-class examples that have utilized this research to effectively optimize social media for fan engagement.
Why Online Advertising?
With the advancement of internet , web has become the most preferred medium for the businesses to promote their brands and services.
Traditional advertising involved putting up hoardings , billboards , distributing pamphlet hoping that the customers would view them. But there wasn't any way to measure the success of the advertisement.
To Know more about online advertising, visit: http://www.knowonlineadvertising.com
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.
Internet advertising an interplay among advertisers,online publishers, ad exc...Trieu Nguyen
Internet Advertising An Interplay among Advertisers,Online Publishers, Ad Exchanges and Web Users, Computational Advertising, contextual advertising, sponsored search, user behaviour targeting
Data-driven Reserve Prices for Social Advertising Auctions at LinkedInKun Liu
Online advertising auctions constitute an important source of revenue for search engines such as Google and Bing, as well as social networks such as Facebook, LinkedIn and Twitter. We study the problem of setting the optimal reserve price in a Generalized Second Price auction, guided by auction theory with suitable adaptations to social advertising at LinkedIn. Two types of reserve prices are deployed: one at the user level, which is kept private by the publisher, and the other at the audience segment level, which is made public to advertisers. We demonstrate through field experiments the effectiveness of this reserve price mechanism to promote demand growth, increase ads revenue, and improve advertiser experience.
What is Paid Search Advertising?
Paid search advertising with Google Ads is among the most effective channels in modern marketing. However, you can only reap the rewards when you understand what you are doing.
• Stating the case for display as a relevant advertising strategy
• Campaign planning and KPIs to measure
• Pricing and ad units to consider
• Targeting tactics and retargeting overview
• Mobile web and app campaigns
• Campaign optimization strategies
• A tour of the Google Display Network platform
• B2B topics; lead nurturing, native advertising, LinkedIn advertising
• Display measurement; View-throughs, attribution modeling
Mobile Marketing: The Next Gen. A presentation by Patrick Lozare at Digital Matters #7 by thumbsup at LaunchPad Bangkok, Thailand on 16 October 2014. The presentation covers details about the current landscape, how to measure performance of mobile advertising and the future. Detailed explanation of programmatic buying and contextual advertising is also covered.
Thomvest Mobile Advertising Overview - February 2016Thomvest Ventures
This is an overview of the mobile adtech ecosystem. Research was conducted by Thomvest Ventures. It covers topics including mobile advertising spend, programmatic advertising, key mobile advertising vendors (i.e DSP, SSP, exchanges & networks), and key trends.
Programmatic advertising is a critical part of any multi-channel recruitment marketing operation.
As the newest kid on the block in recruitment marketing, programmatic sourcing uses ad technology to continually buy, manage, and optimize job ads across the Web, allowing recruiters to get the highest conversion rates.
In this free white paper, we walk you through programmatic to educate you for this inevitable industry shift and explain its key benefits, including:
1. Improve 'apply rate' conversions.
2. Attract quality applicants.
3. Eliminate wasted spend.
Download this white paper to learn how you can start using programmatic to optimize your recruiting funnel for quality candidates and save time and money along the way.
Programatic Media Buying - Introduction by Aum JanakiramAum Jankakiram
When you need to advertise on channels beyond Adwrds and Facebook exchange. You need to use Google DoubleClick or Adobe Advertising Cloud to reach a wider audience with more flexibility.
The last decade has witnessed massive progresses in the field of Artificial Intelligence (AI). With supervision from labelled data, machines have, to some extent, exceeded human-level perception on visual recognitions, while fed with feedback reward, single AI units (aka agents) defeat humans in various games including Atari video games, Go game, and card game. Yet, true human intelligence embraces social and collective wisdom and many real-world AI applications often require multiple AI agents to work in a collaborative effort. A next grand challenge is to answer how large-scale multiple AI agents could learn human-level collaborations, or competitions, from their experiences with the environment where both of their incentives and economic constraints co-exist. In this talk, I shall sample some of our recent research on what is called artificial collective intelligence, ranging from machine bidders competing against each other in an auction environment for buying advertising placements, to image/text/music generation with minimax adversarial games, to coordinating multiple AI agents as a team to defeat their enemies in StarCraft combat games. I will finally conclude the talk by pointing out the future direction on this exciting field.
The first part of the talk will cover our latest research on 1-million-agent reinforcement learning and its potential applications. Our findings show that the dynamics of the population from AI agents, driven by reinforcement learning and self-interest, share a similar pattern as those found in Nature. At the second part of the talk I shall move to a reinforcement learning setting where the game environment is strategic and designable. We present a simple case on how to design a difficult Maze, but the techniques can be used for various applications where the system level objectives are inconsistent with agents’ goals. I will finally conclude the talk by pointing out the future direction on this exciting field of AI.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
3. When
online
adver3sing
goes
wrong
h9p://mashable.com/2008/06/19/contextual-‐adverNsing/
Web
users
were
unlikely
to
click
a
shoes
ad
that
appeared
along
side
an
arNcle
about
the
rather
gruesome
story
about
severed
feet
washing
up
on
shore
4. What
is
the
(fundamental)
problem?
• AutomaNcally
place
the
right
ad
for
the
right
web
page
at
the
right
-me
for
the
right
user
• The
match
is
scienNfically
difficult
– because
ulNmately
ads
shouldn’t
be
matched
to
the
web
pages
where
they
are
to
be
allocated,
– but
the
underlying
web
users,
whose
informaNon
needs
and
reacNons
are,
however,
not
known
a
priori
5. Why
financial
methods?
• Our
observaNons
3+
years
ago:
– although
mulNple
parNcipants
from
ad
providers,
adver-sers,
and
web
users,
the
industry
and
research
seem
unbalanced
• It
is
unclear
who
should
do
that.
“search
engines”
as
publishers,
ad
networks,
doing
the
keyword
matching
etc
• li9le
research
has
been
found
from
the
bidders
(the
adverNsers)’s
perspecNve
to
help
them
manage
their
campaigns
6. An
analogy
with
Financial
markets
• Ads
(display
opportuniNes)
are
“traded”
based
on
the
dual
force
of
supply
(publishers)
and
demand
(adverNsers)
Display
OpportuniNes
=?
“raw
materials”
like
petroleum
and
natural
gas
7. Ads
prices
are
vola3le
(a) (b)The
price
movement
of
a
display
opportunity
from
Yahoo!
ads
data
Under
GSP
(generalized
second
price
aucNon)
(a)
(c)
8. Research
opportuni3es
• Need
Ad’s
Futures
Contract
and
Risk-‐reduc4on
Capabili4es
– AutomaNon
is
constrained
mainly
to
“spots”
markets,
i.e.,
any
transacNon
where
delivery
takes
place
right
away
– No
principled
technologies
to
support
efficient
forward
pricing
&risk
management
mechanisms
• If
we
got
Futures
Market,
adverNsers
could
lock
in
the
campaign
cost
and
Publishers
could
lock
in
a
profit
• Need
to
engineer
a
Unified
Ad
Exchange
– The
ad
market
is
in
the
hands
of
a
few
key
players.
Each
individual
player
defines
its
own
ad
system
– Arbitrage
opportuniNes
exist.
Two
display
opportuniNes
with
similar
targeted
audiences
and
visit
frequency
may
sell
for
quite
different
prices
on
two
different
markets.
9. Current
trends
in
online
adver3sing
• Spot
markets:
Real-‐Time
Bidding
(RTB)
allows
selling
and
buying
online
display
adverNsing
in
real-‐Nme
one
ad
impression
at
a
Nme
•
Future
markets:
ProgrammaNc
Premium
to
use
automated
procedures
to
reach
agreement
of
sales
between
buyers
(adverNsers)
and
sellers
(publishers)
• With
the
two
trends,
a
step
closer
to
the
financial
markets
where
unificaNon
and
interconnecNon
are
strongly
promoted
10. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
11. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
14. Real-‐3me
bidding
“This
is
Lawrence
from
India.
I
was
searching
Recommender
model
in
web
and
found
your
webpage
in
search
engine.
Then,
I
visited
your
webpage
searching
relevant
contents
and
saw
unrelevant
Google
add
in
"Research
Team"
page
(aRached
screenshot).
This
add
might
vary
from
country
to
country.
But
I
feel
it
will
mislead
and
give
wrong
opinion
to
users
who
visit
your
webpage.”
-‐
Lawrence
from
India
15. The
winning
bids
and
hourly
average
Figure 7: The time series snippet of winning bids and its hourly
average of a single placement. The hourly average series peaks
around 6-8am every day when there are less impressions but more
Figure 8: The d
against hour-of
16. Daily
Pacing
ons but more
Wilk test [26]
the number
tisers spend
iser submits
from spend-
) and as fast
may lead to
depletes too
r in the day,
tance of pre-
The uniform
f high qual-
, the pacing
m; if there is
the pacing
get (usually
d daily pac-
Ps.
er of bidders
ised respec-
series reach
e number of
against hour-of-day. Their correlation plot shows a clear lag of
when they reach the maximum in a day. This lag indicates the
unbalance of supply and demand of the market in certain hours.
Besides, the fact that there are more bidders in the morning may
be due to the mixture of hour-of-day targeting and no daily pacing
setup. The plots used 3 months worth of data sampled from a
single placement. Note for some placements the lag was not very
clear.
Figure 9: An interesting instance we found in the dataset: an
advertiser switched from no pacing to even daily pacing. He was
bidding at a flat CPM. With ad exchanges, the large amount of
choose
from
spending
it
evenly
throughout
a
day
(uniform
pacing)
and
as
fast
as
possible
(no
pacing).
17. The
frequency
factor
• How
many
Nmes
ads
(a.k.a.
creaNves)
would
be
displayed
to
a
single
user.
llo-
ent
n a
nta-
red
g
nd-
ery
ors
re-
18. The
Recency
Factor
• helps
to
decide
to
bid
or
not
based
on
how
recently
the
ad
was
displayed
to
the
same
user.
19. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
20. Why
Ad
Futures
(1)
• Suppose
there
is
a
travel
insurance
company
whose
major
customers
are
found
through
online
adverNsing
21. Why
Ad
Futures
(2)
• In
March
the
company
plans
an
adverNsement
campaign
in
three
months
Nme
as
they
think
there
will
be
more
opportuniNes
to
sell
their
travel
insurance
products
in
the
summer
22. Why
Ad
Futures
(3)
• If
the
company
worries
that
the
future
price
of
the
impressions
will
go
up,
they
could
hedge
the
risk
(lock
in
the
campaign
cost)
by
agreeing
to
buy
(taking
a
long
posiNon)
the
display
impressions
in
3
months
Nme
for
an
agreed
price
(taking
a
long
posiNon
in
a
3-‐
month
forwarding
market).
23. Why
Ad
Futures
(4)
• Equally,
search
engines
and
large
publishers
could
agree
to
sell
(taking
a
short
posiNon)
the
display
impressions
in
the
future
if
worry
the
price
will
go
down
(lock
in
a
profit).
• IntuiNvely,
also
useful
for
inventory
management
24. However,
online
adver3sing
is
different
• A.
Non-‐storability:
unlike
stocks
or
other
common
communiNes
(petroleum
and
natural
gas
),
we
cannot
buy
and
keep
an
impression
(thus
ad
display
slot)
for
a
period
of
Nme
and
sell
it
later
in
order
to
gain
the
profit
by
its
price
moment
– This
is
in
fact
more
similar
to
electricity/energy
markets
• B.
Not
just
about
the
price
movements:
the
risk
also
lies
in
the
uncertainty
of
the
number
of
impressions
(number
of
visits)
and
click-‐through
rate
in
the
future
25. C.
There
are
many
pricing
schemes
Based
on
cost
per
click,
cost
per
impression
etc.
However,
online
adver3sing
is
different
26. Ad
Op3ons
Pricing
is
the
Core
• Due
to
the
differences
menNoned,
financial
models
and
theories
cannot
be
directly
employed
–
need
to
rethink
what
is
the
“fair”
price
for
ads
• We
have
developed
a
novel
ad
opNon
pricing
model
to
understand
a
“fair”
price
in
various
senngs,
e.g.,
sponsored
search,
contextual
adverNsing,
and
banner
ads
27. GSP-Based Keyword Auction
Search
engine
Online
adver3sers
1st
ad
slot
£
0.2
£
0.32
£
0.15
£
0.22
2nd
ad
slot
…
£
0.2
£
0.22
Choose
the
adverNser
with
the
highest
bid,
but
normally
charge
him
based
on
the
second
highest
bid
price,
called
Generalised
Second
Price
(GSP)
auc3on
model.
28. • VolaNlity
in
revenue.
• Uncertainty
in
the
bidding
and
charged
prices
for
adverNsers'
keywords.
• Weak
brand
loyalty
between
the
adverNser
and
the
search
engine.
Problems of Auction Mechanism
30. An
opNon
is
a
contract
in
which
the
opNon
seller
grants
the
opNon
buyer
the
right
but
not
the
obligaNon
to
enter
into
a
transacNon
with
the
seller
to
either
buy
or
sell
an
underlying
asset
at
a
specified
price
on
or
before
a
specified
date.
The
specified
price
is
called
strike
price
and
the
specified
date
is
called
expiraNon
date.
The
opNon
seller
grants
this
right
in
exchange
for
a
certain
amount
of
money
at
the
current
Nme
is
called
opNon
price.
Option Contract
31. online
adver3ser
search
engine
sell
a
list
of
ad
keywords
via
a
mulN-‐keyword
mulN-‐click
opNon
mul3-‐keyword
mul3-‐click
op3on
(3
month
term)
upfront
fee
(m
=
100)
keywords
list
fixed
CPCs
£5
‘MSc
Web
Science’
£1.80
‘MSc
Big
Data
AnalyNcs’
£6.25
‘Data
Mining’
£8.67
t
=
T
Timeline
submit
a
request
of
guaranteed
ad
delivery
for
the
keywords
‘MSc
Web
Science’,
‘MSc
Big
Data
AnalyNcs’
and
‘Data
Mining’
for
the
future
3
month
term
[0,
T],
where
T
=
0.25.
t
=
0
pay
£5
upfront
opNon
price
to
obtain
the
opNon.
Selling & Buying An Option
32. 3232
online
adver3ser
search
engine
t
=
T
Timeline
exercise
100
clicks
of
‘MSc
Web
Science’
via
opNon.
t
=
0
pay
£1.80
to
the
search
engine
for
each
click
unNl
the
requested
100
clicks
are
fully
clicked
by
Internet
users.
t
=
t1
reserve
an
ad
slot
of
the
keyword
‘MSc
Web
Science’
for
the
adverNser
for
100
clicks
unNl
all
the
100
clicks
are
fully
clicked
by
Internet
users..
t
=
t1c
Exercising the Option
33. 3333
online
adver3ser
search
engine
t
=
T
Timeline
if
the
adverNser
thinks
the
fixed
CPC
£8.67
of
the
keyword
‘Data
Mining’
is
expensive,
he/she
can
a9end
keyword
aucNons
to
bid
for
the
keyword
as
other
bidders,
say
£8.
t
=
0
pay
the
GSP-‐based
CPC
for
each
click
if
winning
the
bid.
t
=
…
select
the
winning
bidder
for
the
keyword
‘Data
Mining’
according
to
the
GSP-‐based
aucNon
model.
Not Exercising the Option
34. Benefits of Ad Option
Adver3ser
Search
Engine
§ secure
ad
service
delivery
§ reduce
uncertainty
in
aucNons
§ caps
ad
cost.
§ selling
the
inventory
in
advance;
§ having
a
more
stable
and
predictable
revenue
over
a
long-‐term
period;
§ Increasing
adverNsers’
loyalty
35. 3535
§ No-‐arbitrage
[F.Black
and
M.Scholes1973;
H.Varian1994]
§ StochasNc
underlying
keyword
CPC
[P.
Samuelson1965]
§ Terminal
value
formulaNon
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
Ad Option Pricing: Building Blocks
36. Ad Option Pricing: Formula
§ n=1,
Black-‐Scholes-‐Merton
European
call
§ n=2,
Peter
Zhang
dual
strike
European
call
§ n>=3,
Monte
Carlo
method
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
37. Ad
op3on
pricing
for
the
keywords
`canon
cameras',
`nikon
camera‘
and
`yahoo
web
hos3ng‘.
Experimental Results:
Monte Carlo Simulation
38. Fig.
empirical
example
for
the
keyword
‘lawyer’,
where
the
Shapiro-‐Wilk
test
is
with
p-‐value
0.1603
and
the
Ljung-‐Box
test
is
with
p-‐value
0.6370.
39. 3939
About 15.73% of keywords that can be effectively
priced into an option contract under the GBM.
44. Concluding
remarks:
“Recipe
for
Disaster”-‐
Model
That
“Killed”
Wall
Street?
• Wall
Street’s
math
models
for
minNng
money
worked
brilliantly...
unNl
one
of
them
devastated
the
global
economy
• David
X.
Li's
Gaussian
copula
funcNon
as
first
published
in
2000.
Investors
exploited
it
as
a
quick
way
to
assess
risk
(the
chance
a
company
is
likely
to
default)
• As
Li
himself
said
of
his
own
model:
"The
most
dangerous
part
is
when
people
believe
everything
coming
out
of
it.”
44
h9p://www.wired.com/techbiz/it/magazine/17-‐03/wp_quant?currentPage=all
45. For
more
informa3on,
please
refer
to
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980