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This is a tutorial given in the International Conference on Machine Learning. The slides consist of four parts. Please look for Part 1, Part 2 and Part 4 to get a complete picture of this technology.
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Â
This is a tutorial given in the International Conference on Machine Learning. The slides consist of four parts. Please look for Part 1, Part 3 and Part 4 to get a complete picture of this technology.
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This document discusses techniques for webpage personalization and user profiling. It describes common properties of web personalization problems, including optimizing metrics like click-through rate (CTR) using large-scale sparse data. It then covers online logistic regression (OLR) and generalized matrix factorization (GMF) frameworks for CTR prediction. Experimental results on real-world datasets show that user profiles generated from matrix factorization models can provide significant click lifts over other profile methods when used as features for OLR.
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- A framework to discuss the multi-faceted concerns of cross-domain security (CDS), including infrastructure, information, workflow, application, and policy aspects.
- CDS participants like security domains, security monitors, mission applications, and security guards. Guards are associated with a single security domain.
- Design decisions around guards being aware participants in workflows and trusting each other through mutual authentication.
- Opportunities to standardize interactions through CDS protocols for application interfaces, inter-guard coordination, security monitor interfaces, and a CDS ontology.
This tutorial discusses recommender problems for web applications, focusing on content optimization and match-making. It covers both offline and online components for recommendation systems. The offline components include collaborative filtering methods and techniques for cold starts. The online components involve time-series modeling, online/incremental methods, and explore/exploit approaches. An example application is the Today module on Yahoo's homepage, which uses statistical models trained offline and online to select articles, achieving over double the click-through rate.
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
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Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout and distributed computing frameworks. He also covers strategies for taking recommendation systems into production.
Recommender Systems Tutorial (Part 3) -- Online ComponentsBee-Chung Chen
Â
This is a tutorial given in the International Conference on Machine Learning. The slides consist of four parts. Please look for Part 1, Part 2 and Part 4 to get a complete picture of this technology.
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Â
This is a tutorial given in the International Conference on Machine Learning. The slides consist of four parts. Please look for Part 1, Part 3 and Part 4 to get a complete picture of this technology.
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This document discusses techniques for webpage personalization and user profiling. It describes common properties of web personalization problems, including optimizing metrics like click-through rate (CTR) using large-scale sparse data. It then covers online logistic regression (OLR) and generalized matrix factorization (GMF) frameworks for CTR prediction. Experimental results on real-world datasets show that user profiles generated from matrix factorization models can provide significant click lifts over other profile methods when used as features for OLR.
The document proposes a cross-domain security reference architecture with the following key elements:
- A framework to discuss the multi-faceted concerns of cross-domain security (CDS), including infrastructure, information, workflow, application, and policy aspects.
- CDS participants like security domains, security monitors, mission applications, and security guards. Guards are associated with a single security domain.
- Design decisions around guards being aware participants in workflows and trusting each other through mutual authentication.
- Opportunities to standardize interactions through CDS protocols for application interfaces, inter-guard coordination, security monitor interfaces, and a CDS ontology.
This tutorial discusses recommender problems for web applications, focusing on content optimization and match-making. It covers both offline and online components for recommendation systems. The offline components include collaborative filtering methods and techniques for cold starts. The online components involve time-series modeling, online/incremental methods, and explore/exploit approaches. An example application is the Today module on Yahoo's homepage, which uses statistical models trained offline and online to select articles, achieving over double the click-through rate.
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Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout. He also covers strategies for taking recommendation systems into production.
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In this presentation I will talk about the design of scalable recommender systems and its similarity with advertising systems. The problem of generating and delivering recommendations of content/products to appropriate audiences and ultimately to individual users at scale is largely similar to the matching problem in computational advertising, specially in the context of dealing with self and cross promotional content. In this analogy with online advertising a display opportunity triggers a recommendation. The actors are the publisher (website/medium/app owner) the advertiser (content owner or promoter), whereas the ads or creatives represent the items being recommended that compete for the display opportunity and may have different monetary value to the actors. To effectively control what is recommended to whom, targeting constraints need to be defined over an attribute space, typically grouped by type (Audience, Content, Context, etc.) where some associated values are not known until decisioning time. In addition to constraints, there are business objectives (e.g. delivery quota) defined by the actors. Both constraints and objectives can be encapsulated into and expressed as campaigns. Finally, there there is the concept of relevance, directly related to users' response prediction that is computed using the same attribute space used as signals.
As in advertising, recommendation systems require a serving platform where decisioning happens in real-time (few milliseconds) typically selecting an optimal set of items to display to the user from hundreds, sometimes thousands or millions of items. User actions are then taken as feedback and used to learn models that dynamically adjust order to meet business objectives.
This is a radical departure from the traditional item-based and user-based collaborative filtering approach to recommender systems, which fails to factor-in context, such as time-of-day, geo-location or category of the surrounding content to generate more accurate recommendations. Traditional approaches also fail to recognize that recommendations don't happen in a vacuum and as such may require the evaluation of business constraints and objectives. All this should be considered when designing and developing true commercial recommender/advertising systems.
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The document discusses how AI is used at scale to create professional opportunities. It provides an overview of how AI powers the user and customer experience on LinkedIn through search, recommendations, staying informed, and getting hired. It describes how AI uses profile and network data to improve recommendations through understanding member characteristics and connections. The document also discusses how LinkedIn's recommendation system works, including using a generalized additive mixed-effect model called GLMix for large-scale regression to provide personalized job recommendations.
This document discusses various approaches for designing effective preference elicitation systems for recommendation engines. It covers challenges like the cold start problem and how to ask users questions to understand their preferences. It also examines different types of interfaces, factors that influence user opinions, and strategies for choosing representative examples to elicit preferences efficiently and accurately. The document concludes with discussing evaluation metrics and opportunities for future work.
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http://kdd2012.sigkdd.org/indexpo.shtml#posse
Abstract: By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems.
This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virility preserving online testing and virtual profiling.
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1) The document discusses the evolution of search engines and algorithms over time from early concepts like Hilltop and PageRank to more modern techniques like RankBrain that use neural networks.
2) It also examines how search engines have incorporated personalization and contextualization by using implicit and explicit user data and feedback to better understand search intent and tailor results.
3) Several studies summarized found that most users expect to find information within the first 2 minutes of searching, spend little time viewing individual results, and refine queries through an iterative process as understanding develops.
Data science can provide benefits for digital commerce companies through personalized recommendations, dynamic pricing, and search engine optimization. At Info Edge India, which operates job, real estate, and matrimonial sites, data science is used for applications like real-time recommendation engines, lead scoring, price trend analysis, and semantic search. Data science techniques employed include machine learning, text mining, natural language processing, and using big data technologies and nosql databases.
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Mechanical Turk allows businesses to leverage an on-demand global workforce to perform tasks requiring human intelligence like data entry, content moderation, and search query classification. Requesters can create and publish tasks through the Mechanical Turk web interface or API and workers are paid micro-payments for completing work. Common uses include search marketing, content creation and management, and data verification. Case studies show it can quickly scale workforces and is cost effective.
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- Developing hypotheses by gathering data on users' motivations, the relevance of content, and optimal website structures.
- Analyzing test results and focusing on key performance indicators to continuously improve advertising.
The Narrative Mind team seeks to develop tools to optimize discovery and investigation of communication trends on social media. They have conducted 66 interviews total with experts, users, and potential buyers. The team hypothesizes that their narrative detection units may overlap with other commercial tools, and plan to do a demo day to compare capabilities. They have proposed an MVP approach of outputting 3 types of narratives to analysts based on frequency of units associated with real-world narratives.
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Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
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The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
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Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. đź’»
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
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The document discusses how AI is used at scale to create professional opportunities. It provides an overview of how AI powers the user and customer experience on LinkedIn through search, recommendations, staying informed, and getting hired. It describes how AI uses profile and network data to improve recommendations through understanding member characteristics and connections. The document also discusses how LinkedIn's recommendation system works, including using a generalized additive mixed-effect model called GLMix for large-scale regression to provide personalized job recommendations.
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1) The document discusses the evolution of search engines and algorithms over time from early concepts like Hilltop and PageRank to more modern techniques like RankBrain that use neural networks.
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
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During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
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Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
4. Three components we will focus on today
• Defining the problem
– Formulate objectives whose optimization achieves some long-
term goals for the recommender system
• E.g. How to serve content to optimize audience reach and engagement,
optimize some combination of engagement and revenue ?
• Modeling (to estimate some critical inputs)
– Predict rates of some positive user interaction(s) with items based
on data obtained from historical user-item interactions
• E.g. Click rates, average time-spent on page, etc
• Could be explicit feedback like ratings
• Experimentation
– Create experiments to collect data proactively to improve models,
helps in converging to the best choice(s) cheaply and rapidly.
• Explore and Exploit (continuous experimentation)
• DOE (testing hypotheses by avoiding bias inherent in data)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 4
5. Modern Recommendation Systems
• Goal
– Serve the right item to a user in a given context to optimize long-
term business objectives
• A scientific discipline that involves
– Large scale Machine Learning & Statistics
• Offline Models (capture global & stable characteristics)
• Online Models (incorporates dynamic components)
• Explore/Exploit (active and adaptive experimentation)
– Multi-Objective Optimization
• Click-rates (CTR), Engagement, advertising revenue, diversity, etc
– Inferring user interest
• Constructing User Profiles
– Natural Language Processing to understand content
• Topics, “aboutness”, entities, follow-up of something, breaking news,…
Deepak Agarwal & Bee-Chung Chen @ ICML’11 5
6. Some examples from content optimization
• Simple version
– I have a content module on my page, content inventory is obtained
from a third party source which is further refined through editorial
oversight. Can I algorithmically recommend content on this
module? I want to improve overall click-rate (CTR) on this module
• More advanced
– I got X% lift in CTR. But I have additional information on other
downstream utilities (e.g. advertising revenue). Can I increase
downstream utility without losing too many clicks?
• Highly advanced
– There are multiple modules running on my webpage. How do I
perform a simultaneous optimization?
Deepak Agarwal & Bee-Chung Chen @ ICML’11 6
7. Recommend search queries
Recommend packages:
Image
Title, summary
Links to other pages
Pick 4 out of a pool of K
K = 20 ~ 40
Dynamic
Routes traffic other pages
Recommend applications Recommend news article
Deepak Agarwal & Bee-Chung Chen @ ICML’11 7
8. Problems in this example
• Optimize CTR on multiple modules
– Today Module, Trending Now, Personal Assistant, News
– Simple solution: Treat modules as independent, optimize
separately. May not be the best when there are strong correlations.
• For any single module
– Optimize some combination of CTR, downstream engagement,
and perhaps advertising revenue.
Deepak Agarwal & Bee-Chung Chen @ ICML’11 8
9. Online Advertising
Response rates
(click, conversion, ad-view)
Bids
conversion
ML /Statistical Auction
model
Advertisers
Select argmax f(bid,response rates)
Click
Recommend
Ads
Best ad(s)
Ad Network
Page
User •Examples:
Yahoo, Google, MSN, …
Ad exchanges (RightMedia,
DoubleClick, …)
Publisher
Deepak Agarwal & Bee-Chung Chen @ ICML’11 9
10. Recommender problems in general
• Example applications
• Search: Web, Vertical
• Online Advertising Item Inventory
• Content Articles, web page,
•….. ads, …
Context
query, page, …
Use an automated algorithm
to select item(s) to show
Get feedback (click, time spent,..)
USER
Refine the models
Repeat (large number of times)
Optimize metric(s) of interest
(Total clicks, Total revenue,…)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 10
11. Important Factors
• Items: Articles, ads, modules, movies, users, updates, etc.
• Context: query keywords, pages, mobile, social media, etc.
• Metric to optimize (e.g., relevance score, CTR, revenue, engagement)
– Currently, most applications are single-objective
– Could be multi-objective optimization (maximize X subject to Y, Z,..)
• Properties of the item pool
– Size (e.g., all web pages vs. 40 stories)
– Quality of the pool (e.g., anything vs. editorially selected)
– Lifetime (e.g., mostly old items vs. mostly new items)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 11
12. Factors affecting Solution (continued)
• Properties of the context
– Pull: Specified by explicit, user-driven query (e.g., keywords, a form)
– Push: Specified by implicit context (e.g., a page, a user, a session)
• Most applications are somewhere on continuum of pull and push
• Properties of the feedback on the matches made
– Types and semantics of feedback (e.g., click, vote)
– Latency (e.g., available in 5 minutes vs. 1 day)
– Volume (e.g., 100K per day vs. 300M per day)
• Constraints specifying legitimate matches
– e.g., business rules, diversity rules, editorial Voice
– Multiple objectives
• Available Metadata (e.g., link graph, various user/item attributes)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 12
13. Predicting User-Item Interactions (e.g. CTR)
• Myth: We have so much data on the web, if we can only
process it the problem is solved
– Number of things to learn increases with sample size
• Rate of increase is not slow
– Dynamic nature of systems make things worse
– We want to learn things quickly and react fast
• Data is sparse in web recommender problems
– We lack enough data to learn all we want to learn and as quickly
as we would like to learn
– Several Power laws interacting with each other
• E.g. User visits power law, items served power law
– Bivariate Zipf: Owen & Dyer, 2011
Deepak Agarwal & Bee-Chung Chen @ ICML’11 13
14. Can Machine Learning help?
• Fortunately, there are group behaviors that generalize to
individuals & they are relatively stable
– E.g. Users in San Francisco tend to read more baseball news
• Key issue: Estimating such groups
– Coarse group : more stable but does not generalize that well.
– Granular group: less stable with few individuals
– Getting a good grouping structure is to hit the “sweet spot”
• Another big advantage on the web
– Intervene and run small experiments on a small population to
collect data that helps rapid convergence to the best choices(s)
• We don’t need to learn all user-item interactions, only those that are good.
Deepak Agarwal & Bee-Chung Chen @ ICML’11 14
15. Predicting user-item interaction rates
Feature construction
Content: IR, clustering, taxonomy, entity,..
User profiles: clicks, views, social, community,..
Offline Online
( Captures stable characteristics Initialize (Finer resolution
at coarse resolutions) Corrections)
(Logistic, Boosting,….) (item, user level)
(Quick updates)
Explore/Exploit
(Adaptive sampling)
(helps rapid convergence
to best choices)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 15
16. Post-click: An example in Content Optimization
Recommender •
EDITORIAL
AD SERVER
Clicks on FP links influence DISPLAY
downstream supply distribution
content ADVERTISING Revenue
Downstream
engagement
(Time spent)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 16
17. Serving Content on Front Page: Click Shaping
• What do we want to optimize?
• Current: Maximize clicks (maximize downstream supply from FP)
• But consider the following
– Article 1: CTR=5%, utility per click = 5
– Article 2: CTR=4.9%, utility per click=10
• By promoting 2, we lose 1 click/100 visits, gain 5 utils
• If we do this for a large number of visits --- lose some clicks but obtain
significant gains in utility?
– E.g. lose 5% relative CTR, gain 40% in utility (revenue, engagement, etc)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 17
18. Example Application:
Today Module on Yahoo! Homepage
Currently in production, powered by some methods
discussed in this tutorial
19. Recommend packages:
Image
Title, summary
1 2 3 4 Links to other pages
Pick 4 out of a pool of K
K = 20 ~ 40
Dynamic
Routes traffic other pages
Deepak Agarwal & Bee-Chung Chen @ ICML’11 19
20. Problem definition
• Display “best” articles for each user visit
• Best - Maximize User Satisfaction, Engagement
– BUT Hard to obtain quick feedback to measure these
• Approximation
– Maximize utility based on immediate feedback (click rate) subject
to constraints (relevance, freshness, diversity)
• Inventory of articles?
– Created by human editors
– Small pool (30-50 articles) but refreshes periodically
Deepak Agarwal & Bee-Chung Chen @ ICML’11 20
21. Where are we today?
• Before this research
– Articles created and selected for display by editors
• After this research
– Article placement done through statistical models
• How successful ?
"Just look at our homepage, for example. Since we began pairing our content
optimization technology with editorial expertise, we've seen click-through rates
in the Today module more than double. ----- Carol Bartz, CEO Yahoo! Inc (Q4,
2009)
Deepak Agarwal & Bee-Chung Chen @ ICML’11 21
22. Main Goals
• Methods to select most popular articles
– This was done by editors before
• Provide personalized article selection
– Based on user covariates
– Based on per user behavior
• Scalability: Methods to generalize in small traffic scenarios
– Today module part of most Y! portals around the world
– Also syndicated to sources like Y! Mail, Y! IM etc
Deepak Agarwal & Bee-Chung Chen @ ICML’11 22
23. Similar applications
• Goal: Use same methods for selecting most popular, personalization
across different applications at Y!
• Good news! Methods generalize, already in use
Deepak Agarwal & Bee-Chung Chen @ ICML’11 23
24. Next few hours
Most Popular Personalized
Recommendation Recommendation
Offline Models Collaborative filtering
(cold-start problem)
Online Models Time-series models Incremental CF,
online regression
Intelligent Initialization Prior estimation Prior estimation,
dimension reduction
Explore/Exploit Multi-armed bandits Bandits with covariates
Deepak Agarwal & Bee-Chung Chen @ ICML’11 24
Editor's Notes
This only shows one scenario; that of content match. Let’s add Sponsored Search (Replace Content with Query) and Have a new slide for display advertising. This also does not provide info for the revenue model (shall we add it here or later).