Advertising Sciences is an inter-disciplinary field that studies the dynamics of an ecosystem of users, publishers, advertisers, and ad networks. Its central problem is to find the “best” matching ads to a user in a given context (e.g., query, page view) that optimize the utilities of the participants in the ecosystem under certain business constraints (blocking, targeting, guaranteed delivery, etc) by applying cutting-edge algorithms and techniques in information retrieval, machine learning, data mining, optimization, and micro-economics.
Featured Project
Team
- A. Bagherjeiran
- Adrian Silvescu
- A. Ratnaparkhi
- Ahmed Asif
- Ajay Shekhawat
- Albert Meltzer
- Allan Zhang
- Anand Kesari
- Anand Murugappan
- Andrew Hatch
- Anish Nair
- A. Seetharaman
- Ashvin Kannan
- Benjamin Rey
- Bruce Robbins
- C. Nagarajan
- Chris Bartels
- Chris Leggetter
- Chris LuVogt
- Danfeng Li
- Datong Chen
- Dinesh Garg
- Divy Kothiwal
- Dongni Chen
- Dragomir Yankov
- Dustin Hillard
- Eren Manavoglu
- Erick Cantu-Paz
- Haibin Cheng
- H. Bommaganti
- Hema Raghavan
- H. Swetha Koppula
- Jason Zien
- Jerry Tang
- J. Mao
- Jianzhen Jean
- Jignesh Parmar
- Jimming Cheng
- Jimmy Yang
- Jinhui Liu
- Kannan Achan
- Karim Filali
- Kevin Chang
- Kin Fai Kan
- K. Leela Poola
- K. Prasad Chitrapura
- Kun Liu
- Leo Neumeyer
- Long Ji
- Marcin Mejran
- Maurício Mediano
- Mehul Parsana
- Minyao Zhu
- Nagaraj Kota
- Naiping Liu
- N. L. Bhamidipati
- Ozgur Cetin
- P. Marulappa
- Peiji Chen
- Piyoosh Jalan
- P. Krishnamurthy
- Rajen Subba
- Rajesh Parekh
- Rajiv Ratnam
- Rukmini Iyer
- R. Zhang
- Rushi Bhatt
- Sachin Garg
- S. Kshetramade
- S. Parthasarathy
- Sharath Rao
- S. Mukherjee
- S. Manohar Singh
- Siwei Jia
- Song Lin
- S. Bhattacharya
- S. Venkatesan
- Stefan Schroedl
- Sudip Khanna
- Sunil Jagadish
- Tasos Anastasakos
- Utku Irmak
- Vadim von Brzeski
- Vijay Murthi
- Vijay Narayanan
- Wanlin Pang
- Wei Li
- Wei Ye
- Xuerui Wang
- Yang Yu
- Yaping Shi
- Yayati Kasralikar
- Yefei Peng
- Ying Cui
- Zengyan Zhang
Projects
Real-Time MapReduce S4 is a real-time MapReduce software platform that is used to process massive streams of data. The information is used to improve search and advertising experience by providing fresh and personalized results to consumers and advertisers.
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Ad Indexing & Retrieval Displaying ads alongside web queries is a very effective advertising approach. YLabs is working on better ways to index large advertiser databases and performing efficient and precision driven real-time retrieval for serving sponsored search ads.
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Estimation of Reserve Price for Sponsored Search Auctions Reserve Pricing is an important feature of an auction marketplace. offers several benefits such as offering price support and improving the user experience by eliminating ads with poor relevance
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Machine Learned Categorization for Ads and QueriesMachine Learned Categorization efforts at Y! labs focus research and development of automated methods of categorizing the key entities in advertising - users, content, queries, and ads to facilitate superior matching and improved relevance.
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Predicting Query to Ad Relevance Sponsored search needs to satisfy both the search users, by providing high quality advertisements that are relevant to the user, and the advertiser, by driving customers with a buying intent to their site.
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Display Supply & Demand ForecastingY! Labs is developing the state-of-the-art forecasting algorithms to predict future supply of target-able page-views and demand from advertisers
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Mapping Search Query Language to Advertiser Bidded Terms Sponsored search is aimed at connecting search advertisers to people who may be interested in their products, but advertisers and consumers don't always speak the same language.
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Contextual Ads Relevance Modeling Y! Labs is developing advanced information retrieval techniques to improve contextual ads relevance.
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Contextual Advertising Y! Labs is pioneering advances in contextual advertising by building highly scalable ad search systems and predictive models for effectively targeting ads based on the context of the user's browsing behavior and the content of the web pages visited.
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Response Prediction Y! Labs is developing advanced machine learning and statistical modeling techniques to predict user response (click & conversion) to ads impressions given user context.
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Conversion Modeling in Sponsored Search Both advertisers and search users want to increase conversion rates and decrease the cost. This project measures and predicts conversion rates and uses this data to influence ad ranking, pricing and placement, for better value for both search users and advertisers.
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Traffic Quality Measurement & PricingYahoo! Labs is working on a methodology drawing from advanced statistical estimation techniques to measure traffic quality for a heterogeneous mix of publishers.
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Personalization of Click Prediction in Sponsored SearchWe are working on models that give personalized predictions of the response to sponsored search ads. These models will improve the user experience by making changes to ranking and presentation of ads, according to the user's past behavior.
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Generalized Utility in Sponsored Search Auctions Yahoo! Labs is constantly improving Yahoo!’s ad auction system to create long-term value to search engine users, publishers, and advertisers
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Display Inventory Allocation Optimization Inventory allocation plays a critical role in revenue management for the online advertising industry by supporting both Admission Control and Ad Serving.
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KeystoneYahoo! Labs is working on the next generation of contextual advertising technology.
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Internationalization of Click Models Yahoo! Labs is creating a global platform leveraging highly parallel infrastructure to process and train billions of samples to create click prediction models tailored for different global markets.
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User TargetingYahoo! Labs is building a state-of-the-art targeting system that brings the highest quality traffic to advertisers and optimizes revenue for publishers by serving the most relevant ads to users.
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Semantic AnalysisY! Labs is working on inferring user's intent from the content of the page that the user is viewing, in order to provide effective contextual advertising.
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