Prabhakar Raghavan

Prabhakar Raghavan

Prabhakar Raghavan is Google's Chief Technologist.

Prabhakar is one of the foremost authorities on Search and is the co-author of two widely-used graduate texts on algorithms and on search: Randomized Algorithms and Introduction to Information Retrieval. He has over 20 years of research spanning algorithms, web search and databases, published over 100 papers in various fields, and holds 20 issued patents, including several on link analysis for web search.

He joined Google in 2012. Most recently, as Google's Senior Vice President for Knowledge & Information (K&I), Prabhakar was responsible for Google’s Search, Ads, Commerce, Geo, Assistant & Gemini products. Prior to leading K&I, Prabhakar was responsible for the Ads & Commerce teams. Before that, he was Vice President of Workspace in Google Cloud. Before joining Google, Prabhakar founded and led Yahoo! Labs. He also served as CTO at Verity, and was at IBM Research for 14 years.

Prabhakar holds a Ph.D. from U.C. Berkeley in Electrical Engineering and Computer Science and a Bachelor of Technology from the Indian Institute of Technology, Madras. He is a member of the National Academy of Engineering; a Fellow of the ACM and IEEE; a former editor in chief for the Journal of the ACM; and was a Consulting Professor of Computer Science at Stanford University. In 2009, he was awarded a Laurea honoris causa from the University of Bologna. In 2017, the International World Wide Web Conference Committee conferred the 2017 Seoul Test of Time Award to Prabhakar and his co-authors of the 2000 paper "Graph Structure in the Web," recognizing it as a seminal discovery for Web understanding.

Authored Publications
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    The Limits of Popularity-Based Recommendations, and the Role of Social Ties
    Marco Bressan
    Stefano Leucci
    Alessandro Panconesi
    Erisa Terolli
    Proceedings of ACM KDD 2016, ACM
    Preview abstract In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users. View details
    Current trends in the integration of searching and browsing
    Yoelle S. Maarek
    Krishna Bharat
    Susan T. Dumais
    Steve Papa
    Jan O. Pedersen
    WWW (Special interest tracks and posters) (2005), pp. 793
    Preview