3 джозеп курто превращаем вашу организацию в big data компаниюantishmanti
The document discusses transforming an organization into a Big Data company. It outlines the challenges of digital disruption and how companies like Amazon, Apple, Google and Netflix understand customers through their digital footprints. It then discusses six challenges of Big Data including data capture, storage, analysis, visualization, IT dependence, and creating a new culture. The remainder of the document focuses on business models for Big Data and implementing Big Data strategies and projects within an organization.
Criteo provides targeted display advertising that is able to predict user clicks. It buys large advertising inventories from publishers using a cost-per-million (CPM) model and sells campaigns to advertisers on a cost-per-click (CPC) model. Criteo's Real-Time Audience solution allows it to drop cookies and show targeted ads to iOS and Safari users. This solution has increased revenue for publishers by over 100% on average by enabling more accurate frequency capping, higher volume, and price points. Integrating Criteo's RTA solution is described as easy and having no impact on page loading times.
3 джозеп курто превращаем вашу организацию в big data компаниюantishmanti
The document discusses transforming an organization into a Big Data company. It outlines the challenges of digital disruption and how companies like Amazon, Apple, Google and Netflix understand customers through their digital footprints. It then discusses six challenges of Big Data including data capture, storage, analysis, visualization, IT dependence, and creating a new culture. The remainder of the document focuses on business models for Big Data and implementing Big Data strategies and projects within an organization.
Criteo provides targeted display advertising that is able to predict user clicks. It buys large advertising inventories from publishers using a cost-per-million (CPM) model and sells campaigns to advertisers on a cost-per-click (CPC) model. Criteo's Real-Time Audience solution allows it to drop cookies and show targeted ads to iOS and Safari users. This solution has increased revenue for publishers by over 100% on average by enabling more accurate frequency capping, higher volume, and price points. Integrating Criteo's RTA solution is described as easy and having no impact on page loading times.
The document provides tips for running successful performance display marketing campaigns. It outlines 5 common mistakes to avoid: 1) Not defining clear goals and KPIs, 2) Focusing on placements over understanding customer journeys, 3) Not asking the right questions of data to gain insights, 4) Allowing low quality inventory like bot traffic, and 5) Not optimizing campaigns for mobile users. Following people across devices, prioritizing premium inventory, and gaining insights from smart data analysis are some of the keys to driving maximum performance.
This document discusses Criteo's performance advertising solutions. It highlights that Criteo works with over 9,000 publishers and 7,000 advertisers across 130+ countries. Criteo uses a prediction engine and dynamic creative optimization to deliver measurable ROI at unmatched scale across desktop, mobile, in-app, and social platforms. The document also outlines Criteo's solutions for display, email, and cross-device marketing and notes that setup to go live typically takes about 4 weeks with world-class support included.
Aligning with Buyers to Maximize Mobile Revenue presentation by Criteo at DPS...Digiday
This document summarizes a presentation about maximizing revenue from mobile programmatic advertising. It discusses how mobile usage and transactions are growing significantly, but mobile advertising spending has not kept pace. There are barriers to mobile advertising including platform complexity across iOS and Android, lack of standardization, and difficulty with cross-device tracking. The presentation provides recommendations for capitalizing on mobile advertising opportunities, including thinking holistically but optimizing for segments, using mobile as a test bed, and partnering with companies that can provide insights.
Технологии Больших Данных для банков и страховых компаний. Какие задачи решают? Как монетизировать Большие Данные? Бизнес-кейсы и конкретные примеры. Концепция 3D профиля клиента. Точная сегментация и персонифицированный маркетинг. Управление данными на Oracle Big Data Appliance
Criteo's Ad Week 2012 presentation - Big Data and the Value of ClickersCriteo
Big data analytics has shown that the conventional wisdom about display ad clicks being worthless was incorrect. It revealed that people who click on ads (clickers) actually buy 3 times more frequently than non-clickers, and half of all clicks and sales come from just 20% of users. Real-time big data is now being used to serve highly targeted display ads to the right users at the right time with the right message, making the ads actually worth clicking on and representing a $20 billion opportunity for the display advertising industry.
New challenges for scalable machine learning in online advertisingOlivier Koch
The document discusses challenges and opportunities for machine learning in online advertising at scale. It notes that while ML has helped with tasks like bidding and recommendations, challenges remain around long-term effects, overfitting, personalization across devices, and optimal credit assignment and metrics. The document proposes that reinforcement learning, counterfactual analysis, transfer learning and factorization could help address issues like optimal bidding strategies, offline evaluation, and modeling long tail users and products. It concludes by inviting others to help solve remaining open challenges.
Making advertising personal, 4th NL Recommenders MeetupOlivier Koch
Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
Dictionary Learning for Massive Matrix Factorizationrecsysfr
The document presents a new algorithm called Subsampled Online Dictionary Learning (SODL) for solving very large matrix factorization problems with missing values efficiently. SODL adapts an existing online dictionary learning algorithm to handle missing values by only using the known ratings for each user, allowing it to process large datasets with billions of ratings in linear time with respect to the number of known ratings. Experiments on movie rating datasets show that SODL achieves similar prediction accuracy as the fastest existing solver but with a speed up of up to 6.8 times on the largest Netflix dataset tested.
Using Neural Networks to predict user ratingsrecsysfr
The document discusses collaborative filtering and neural network approaches for recommender systems. It introduces collaborative filtering techniques like matrix factorization that aim to predict missing ratings. It then describes applying neural networks to learn nonlinear embeddings of users and items to perform collaborative filtering. The neural network model takes sparse ratings as input, encodes them into a dense representation, then reconstructs the input. It can be trained with both prediction and reconstruction objectives. The model achieves state-of-the-art performance on movie recommendation datasets. Extensions discussed include adding external metadata and using the model for other domains like images.
This document discusses Criteo's C# development workflow, which has evolved over time. Originally, code was split across many repositories with each team responsible for a few. This led to slow change propagation and dependency issues. The new workflow aims for early integration using trunk-based development with all commits on the main branch. A "MOAB" job continuously builds all C# code from latest commits. Developers can check out snapshots from this job or all sources. Pre-submit tests are run before merging changes. Trunk-based development enables continuous delivery but requires strong test coverage and avoids large changes.
CONTENT2VEC: a Joint Architecture to use Product Image and Text for the task ...recsysfr
Thomas Nedelec presented Content2Vec, a joint architecture that uses product images and text for product recommendation. The architecture has three main modules: (1) it represents products using text, images, categories and co-occurrence information; (2) it merges the different representations using metric learning and ensemble methods; (3) experimental results showed it improved recommendations for new products over baselines. The architecture is scalable and modular for production recommender systems.
Meta-Prod2Vec: Simple Product Embeddings with Side-Informationrecsysfr
The document describes Meta-Prod2Vec, a method for embedding products that leverages both co-occurrence information from user sessions as well as side information about products like categories and brands. It improves upon Prod2Vec, which learns embeddings from co-occurrence data alone, by incorporating side information to help address cold start problems when little co-occurrence data exists. Meta-Prod2Vec places additional constraints on embedding distances using the side information, such as enforcing that similar products and their associated metadata like artists be close in the embedding space. This allows it to generate more robust embeddings, especially for cold start products.
1) Criteo is an advertising technology company that uses machine learning to target personalized ads to users across devices.
2) It has over 2000 employees, works with 16,000 publishers and 11,000 advertisers worldwide, and generated $11 billion for its clients in 2015.
3) Criteo's machine learning challenges include selecting the best products and ads to show users from huge catalogs in milliseconds, building a graph connecting billions of devices to identify users, and extensively testing new models and systems.
The document provides tips for running successful performance display marketing campaigns. It outlines 5 common mistakes to avoid: 1) Not defining clear goals and KPIs, 2) Focusing on placements over understanding customer journeys, 3) Not asking the right questions of data to gain insights, 4) Allowing low quality inventory like bot traffic, and 5) Not optimizing campaigns for mobile users. Following people across devices, prioritizing premium inventory, and gaining insights from smart data analysis are some of the keys to driving maximum performance.
This document discusses Criteo's performance advertising solutions. It highlights that Criteo works with over 9,000 publishers and 7,000 advertisers across 130+ countries. Criteo uses a prediction engine and dynamic creative optimization to deliver measurable ROI at unmatched scale across desktop, mobile, in-app, and social platforms. The document also outlines Criteo's solutions for display, email, and cross-device marketing and notes that setup to go live typically takes about 4 weeks with world-class support included.
Aligning with Buyers to Maximize Mobile Revenue presentation by Criteo at DPS...Digiday
This document summarizes a presentation about maximizing revenue from mobile programmatic advertising. It discusses how mobile usage and transactions are growing significantly, but mobile advertising spending has not kept pace. There are barriers to mobile advertising including platform complexity across iOS and Android, lack of standardization, and difficulty with cross-device tracking. The presentation provides recommendations for capitalizing on mobile advertising opportunities, including thinking holistically but optimizing for segments, using mobile as a test bed, and partnering with companies that can provide insights.
Технологии Больших Данных для банков и страховых компаний. Какие задачи решают? Как монетизировать Большие Данные? Бизнес-кейсы и конкретные примеры. Концепция 3D профиля клиента. Точная сегментация и персонифицированный маркетинг. Управление данными на Oracle Big Data Appliance
Criteo's Ad Week 2012 presentation - Big Data and the Value of ClickersCriteo
Big data analytics has shown that the conventional wisdom about display ad clicks being worthless was incorrect. It revealed that people who click on ads (clickers) actually buy 3 times more frequently than non-clickers, and half of all clicks and sales come from just 20% of users. Real-time big data is now being used to serve highly targeted display ads to the right users at the right time with the right message, making the ads actually worth clicking on and representing a $20 billion opportunity for the display advertising industry.
New challenges for scalable machine learning in online advertisingOlivier Koch
The document discusses challenges and opportunities for machine learning in online advertising at scale. It notes that while ML has helped with tasks like bidding and recommendations, challenges remain around long-term effects, overfitting, personalization across devices, and optimal credit assignment and metrics. The document proposes that reinforcement learning, counterfactual analysis, transfer learning and factorization could help address issues like optimal bidding strategies, offline evaluation, and modeling long tail users and products. It concludes by inviting others to help solve remaining open challenges.
Making advertising personal, 4th NL Recommenders MeetupOlivier Koch
Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
Dictionary Learning for Massive Matrix Factorizationrecsysfr
The document presents a new algorithm called Subsampled Online Dictionary Learning (SODL) for solving very large matrix factorization problems with missing values efficiently. SODL adapts an existing online dictionary learning algorithm to handle missing values by only using the known ratings for each user, allowing it to process large datasets with billions of ratings in linear time with respect to the number of known ratings. Experiments on movie rating datasets show that SODL achieves similar prediction accuracy as the fastest existing solver but with a speed up of up to 6.8 times on the largest Netflix dataset tested.
Using Neural Networks to predict user ratingsrecsysfr
The document discusses collaborative filtering and neural network approaches for recommender systems. It introduces collaborative filtering techniques like matrix factorization that aim to predict missing ratings. It then describes applying neural networks to learn nonlinear embeddings of users and items to perform collaborative filtering. The neural network model takes sparse ratings as input, encodes them into a dense representation, then reconstructs the input. It can be trained with both prediction and reconstruction objectives. The model achieves state-of-the-art performance on movie recommendation datasets. Extensions discussed include adding external metadata and using the model for other domains like images.
This document discusses Criteo's C# development workflow, which has evolved over time. Originally, code was split across many repositories with each team responsible for a few. This led to slow change propagation and dependency issues. The new workflow aims for early integration using trunk-based development with all commits on the main branch. A "MOAB" job continuously builds all C# code from latest commits. Developers can check out snapshots from this job or all sources. Pre-submit tests are run before merging changes. Trunk-based development enables continuous delivery but requires strong test coverage and avoids large changes.
CONTENT2VEC: a Joint Architecture to use Product Image and Text for the task ...recsysfr
Thomas Nedelec presented Content2Vec, a joint architecture that uses product images and text for product recommendation. The architecture has three main modules: (1) it represents products using text, images, categories and co-occurrence information; (2) it merges the different representations using metric learning and ensemble methods; (3) experimental results showed it improved recommendations for new products over baselines. The architecture is scalable and modular for production recommender systems.
Meta-Prod2Vec: Simple Product Embeddings with Side-Informationrecsysfr
The document describes Meta-Prod2Vec, a method for embedding products that leverages both co-occurrence information from user sessions as well as side information about products like categories and brands. It improves upon Prod2Vec, which learns embeddings from co-occurrence data alone, by incorporating side information to help address cold start problems when little co-occurrence data exists. Meta-Prod2Vec places additional constraints on embedding distances using the side information, such as enforcing that similar products and their associated metadata like artists be close in the embedding space. This allows it to generate more robust embeddings, especially for cold start products.
1) Criteo is an advertising technology company that uses machine learning to target personalized ads to users across devices.
2) It has over 2000 employees, works with 16,000 publishers and 11,000 advertisers worldwide, and generated $11 billion for its clients in 2015.
3) Criteo's machine learning challenges include selecting the best products and ads to show users from huge catalogs in milliseconds, building a graph connecting billions of devices to identify users, and extensively testing new models and systems.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
