Alexander Tuzhilin

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Alexander Tuzhilin
Alexander Tuzhilin.JPG
Born
Alexander Sergei Tuzhilin

1957 (age 6667)
Alma mater New York University (S.B. 1980)
Stanford University (S.B. 1981)
Courant Institute of Mathematical Sciences (Ph.D. 1989)
Scientific career
Fields Information Systems, data mining
Institutions New York University Stern School of Business
University of the People
Thesis Using relational discrete event systems and models for prediction of future behavior of databases  (1989)
Doctoral advisor Zvi Kedem

Alexander Sergei Tuzhilin (born 1957) is a Professor of Data Science and Information Systems and the Leonard N. Stern Endowed Professor of Business at New York University's Stern School of Business. He also serves as the Dean of Computer Science at the University of the People on the pro bono basis.

Contents

Professor Tuzhilin is known for his work on personalization, recommender systems, machine learning and AI, where he has made several contributions, including being instrumental in developing the area of Context-Aware Recommender Systems (CARS), proposing novel methods of providing unexpected and cross-domain recommendations based on the principles of deep-learning, developing novel approaches to customer segmentation, and discovery of unexpected patterns in data.

Education

Tuzhilin received his B.A. in Mathematics from the New York University in 1980, M.S. in Engineering Economics from the Department of Management Science and Engineering at Stanford University in 1981, and Ph.D. in computer science from NYU's Courant Institute of Mathematical Sciences in 1989, his doctoral advisor being Zvi Kedem. [1] [2]

Career

Tuzhilin joined the faculty at the New York University Stern School of Business in 1989 as an Assistant Professor of Information Systems. He is currently the Leonard N. Stern Professor of Business. He is also the Dean of Computer Science at the University of the People. [3]

Research

Tuzhilin researches data mining in databases, personalization, recommender systems, and customer relationship management. [2]

In 2006, Tuzhilin was hired by Google and given access to its monitoring systems to do a study on click fraud. This was part of a class-action settlement requiring Google to offer advertisers up to $60 million in refunds. Tuzhilin concluded that defining and tracking click fraud will be difficult, because it is often not possible to decipher whether Web surfers were clicking on an advertising link out of malice or as part of an innocent online excursion. [4] [5] [6] [7]

Patents

In 2001, Tuzhilin patented a method of building customer profiles and using them to recommend products and services. Tuzhilin said of the patent, 'It's very broad and very general, and occupies some prime real estate in this space. It essentially covers technologies that are crucial for implementation of customer relationship management.' He added that the patent was careful not to stipulate that the technology was designed for Internet applications. Others pointed out that there were legal exceptions to business methods patents. Any individuals or companies that can show they have been engaged in a business practice for at least a year before a patent application for that practice was filed may be able to circumvent the patent. [8]

In March 2012, Yahoo sued Facebook for violating 10 of its patents. Facebook countersued Yahoo, claiming that it violated Facebook patents that covered 80% of the Yahoo's 2011 revenues. Three of Facebook's patents were originally granted to Tuzhilin. [9] [10] [11]

Related Research Articles

A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Click fraud is a type of fraud that occurs on the Internet in pay-per-click (PPC) online advertising. In this type of advertising, the owners of websites that post the ads are paid based on how many site visitors click on the ads. Fraud occurs when a person, automated script, computer program or an auto clicker imitates a legitimate user of a web browser, clicking on such an ad without having an actual interest in the target of the ad's link in order to increase revenue. Click fraud is the subject of some controversy and increasing litigation due to the advertising networks being a key beneficiary of the fraud.

Pay-per-click (PPC) is an internet advertising model used to drive traffic to websites, in which an advertiser pays a publisher when the ad is clicked.

<span class="mw-page-title-main">Courant Institute of Mathematical Sciences</span> Division of New York University, US (founded 1935)

The Courant Institute of Mathematical Sciences is the mathematics research school of New York University (NYU). Founded in 1935, it is named after Richard Courant, one of the founders of the Courant Institute and also a mathematics professor at New York University from 1936 to 1972, and serves as a center for research and advanced training in computer science and mathematics. It is located on Gould Plaza next to the Stern School of Business and the economics department of the College of Arts and Science.

Personalization consists of tailoring a service or product to accommodate specific individuals, sometimes tied to groups or segments of individuals. Personalization requires collecting data on individuals, including web browsing history, web cookies, and location. Companies and organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization is a key element in social media and recommender systems. Personalization affects every sector of society—work, leisure, and citizenship.

<span class="mw-page-title-main">Targeted advertising</span> Form of advertising

Targeted advertising is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting. These traits can either be demographic with a focus on race, economic status, sex, age, generation, level of education, income level, and employment, or psychographic focused on the consumer values, personality, attitude, opinion, lifestyle and interest. This focus can also entail behavioral variables, such as browser history, purchase history, and other recent online activities. The process of algorithm targeting eliminates waste.

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References

  1. "Alexander Tuzhilin". NYU Stern. Retrieved 2020-04-19.
  2. 1 2 Tuzhilin, Alexander. "CV" (PDF). NYU Stern.
  3. Dr. Alexander Tuzhilin Dean, Computer Science, University of the People
  4. Search giants team up to combat 'click fraud', New York Times, August 2, 2006
  5. Expert's Report Backs Google in Lawsuit Over Click Fraud, Los Angeles Times, July 22, 2006
  6. Report: Google Tries to Fight Click Fraud, Washington Post, July 21, 2006
  7. So Many Hits, So Few Sales, Wall Street Journal, November 13, 2006
  8. A method of collecting consumer data renews questions about patents on business practices, New York Times, July 30, 2001
  9. Facebook Accuses Yahoo of Infringing on Patents, New York Times, April 3, 2012
  10. How Facebook’s Winning The War Against Yahoo, Patent By Patent, TechCrunch, Apr 4, 2012
  11. How Facebook built its legal defence against Yahoo, Financial Times, Apr 03 2012