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Ramakrishnan Srikant

Ramakrishnan Srikant

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    Micro-Browsing Models for Search Snippets
    International Conference on Data Engineering (ICDE), IEEE (2019), pp. 1904-1909
    Preview abstract Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR is the product of the probability of examination times the perceived relevance of the result. Hence there has been considerable work on user browsing models to separate out the examination and relevance components of CTR. However, the snippet text often plays a critical role in the perceived relevance of the result. In this paper, we propose a micro-browsing model for how users read result snippets. We validate the user model by considering the problem of predicting which of two different snippets will yield higher CTR. We show that classification accuracy is dramatically higher with our user model. View details
    Optimizing Budget Constrained Spend in Search Advertising
    Chinmay Karande
    Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013, ACM, pp. 697-706
    Preview abstract Search engine ad auctions typically have a significant fraction of advertisers who are budget constrained, i.e., if allowed to participate in every auction that they bid on, they would spend more than their budget. This yields an important problem: selecting the ad auctions in which these advertisers participate, in order to optimize different system objectives such as the return on investment for advertisers, and the quality of ads shown to users. We present a system and algorithms for optimizing such budget constrained spend. The system is designed be deployed in a large search engine, with hundreds of thousands of advertisers, millions of searches per hour, and with the query stream being only partially predictable. We have validated the system design by implementing it in the Google ads serving system and running experiments on live traffic. We have also compared our algorithm to previous work that casts this problem as a large linear programming problem limited to popular queries, and show that our algorithms yield substantially better results. View details
    User browsing models: relevance versus examination
    Ni Wang
    Daryl Pregibon
    Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Washington, DC (2010), pp. 223-232
    Preview abstract There has been considerable work on user browsing models for search engine results, both organic and sponsored. The click-through rate (CTR) of a result is the product of the probability of examination (will the user look at the result) times the perceived relevance of the result (probability of a click given examination). Past papers have assumed that when the CTR of a result varies based on the pattern of clicks in prior positions, this variation is solely due to changes in the probability of examination. We show that, for sponsored search results, a substantial portion of the change in CTR when conditioned on prior clicks is in fact due to a change in the relevance of results for that query instance, not just due to a change in the probability of examination. We then propose three new user browsing models, which attribute CTR changes solely to changes in relevance, solely to changes in examination (with an enhanced model of user behavior), or to both changes in relevance and examination. The model that attributes all the CTR change to relevance yields substantially better predictors of CTR than models that attribute all the change to examination, and does only slightly worse than the model that attributes CTR change to both relevance and examination. For predicting relevance, the model that attributes all the CTR change to relevance again does better than the model that attributes the change to examination. Surprisingly, we also find that one model might do better than another in predicting CTR, but worse in predicting relevance. Thus it is essential to evaluate user browsing models with respect to accuracy in predicting relevance, not just CTR. View details
    Scaling Up All Pairs Similarity Search
    Roberto Bayardo
    Yiming Ma
    Proc. of the 16th Int'l Conf. on the World Wide Web (2007)
    Preview
    Privacy Preserving OLAP
    Rakesh Agrawal
    Dilys Thomas
    SIGMOD Conference (2005), pp. 251-262
    XPref: a preference language for P3P
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    Computer Networks, vol. 48 (2005), pp. 809-827
    Order-Preserving Encryption for Numeric Data
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    SIGMOD Conference (2004), pp. 563-574
    Whither Data Mining?
    Rakesh Agrawal
    VLDB (2004), pp. 9
    An Implementation of P3P Using Database Technology
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    EDBT (2004), pp. 845-847
    Auditing Compliance with a Hippocratic Database
    Rakesh Agrawal
    Roberto J. Bayardo Jr.
    Christos Faloutsos
    Jerry Kiernan
    Ralf Rantzau
    VLDB (2004), pp. 516-527
    Privacy preserving mining of association rules
    Alexandre V. Evfimievski
    Rakesh Agrawal
    Johannes Gehrke
    Inf. Syst., vol. 29 (2004), pp. 343-364
    Enabling Sovereign Information Sharing Using Web Services
    Rakesh Agrawal
    Dmitri Asonov
    SIGMOD Conference (2004), pp. 873-877
    Information Sharing Across Private Databases
    Rakesh Agrawal
    Alexandre V. Evfimievski
    SIGMOD Conference (2003), pp. 86-97
    An SPath-based preference language for P3P
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    WWW (2003), pp. 629-639
    Searching with Numbers
    Rakesh Agrawal
    IEEE Trans. Knowl. Data Eng., vol. 15 (2003), pp. 855-870
    Implementing P3P Using Database Technology
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    ICDE (2003), pp. 595-606
    Mining newsgroups using networks arising from social behavior
    Rakesh Agrawal
    Sridhar Rajagopalan
    Yirong Xu
    WWW (2003), pp. 529-535
    Limiting privacy breaches in privacy preserving data mining
    Alexandre V. Evfimievski
    Johannes Gehrke
    PODS (2003), pp. 211-222
    Technological Solutions for Protecting Privacy
    Roberto J. Bayardo Jr.
    IEEE Computer, vol. 36 (2003), pp. 115-118
    High-Dimensional Similarity Joins
    Kyuseok Shim
    Rakesh Agrawal
    IEEE Trans. Knowl. Data Eng., vol. 14 (2002), pp. 156-171
    Database Technologies for Electronic Commerce
    Rakesh Agrawal
    Yirong Xu
    VLDB (2002), pp. 1055-1058
    Scaling mining algorithms to large databases
    Paul S. Bradley
    Johannes Gehrke
    Raghu Ramakrishnan
    Commun. ACM, vol. 45 (2002), pp. 38-43
    Data Mining Technologies for Digital Libraries and Web Information Systems
    ICADL (2002), pp. 75
    Hippocratic Databases
    Rakesh Agrawal
    Jerry Kiernan
    Yirong Xu
    VLDB (2002), pp. 143-154
    Privacy Preserving Data Mining: Challenges and Opportunities
    PAKDD (2002), pp. 13
    New Directions in Data Mining
    Mining web logs to improve website organization
    Yinghui Yang
    WWW (2001), pp. 430-437
    Athena: Mining-Based Interactive Management of Text Database
    Rakesh Agrawal
    Roberto J. Bayardo Jr.
    EDBT (2000), pp. 365-379
    Privacy-Preserving Data Mining
    Rakesh Agrawal
    SIGMOD Conference (2000), pp. 439-450
    Discovering Predictive Association Rules
    Nimrod Megiddo
    KDD (1998), pp. 274-278
    High-Dimensional Similarity Joins
    Kyuseok Shim
    Rakesh Agrawal
    ICDE (1997), pp. 301-311
    Range Queries in OLAP Data Cubes
    Ching-Tien Ho
    Rakesh Agrawal
    Nimrod Megiddo
    SIGMOD Conference (1997), pp. 73-88
    Mining Association Rules with Item Constraints
    Quoc Vu
    Rakesh Agrawal
    KDD (1997), pp. 67-73
    Partial Classification Using Association Rules
    Kamal Ali
    Stefanos Manganaris
    KDD (1997), pp. 115-118
    Discovering Trends in Text Databases
    Brian Lent
    Rakesh Agrawal
    KDD (1997), pp. 227-230
    Mining Sequential Patterns: Generalizations and Performance Improvements
    Rakesh Agrawal
    EDBT (1996), pp. 3-17
    Mining Quantitative Association Rules in Large Relational Tables
    Rakesh Agrawal
    SIGMOD Conference (1996), pp. 1-12
    The Quest Data Mining System
    Rakesh Agrawal
    Manish Mehta
    John C. Shafer
    Andreas Arning
    Toni Bollinger
    KDD (1996), pp. 244-249
    Fast Discovery of Association Rules
    Rakesh Agrawal
    Heikki Mannila
    Hannu Toivonen
    A. Inkeri Verkamo
    Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press (1996), pp. 307-328
    Mining Generalized Association Rules
    Rakesh Agrawal
    VLDB (1995), pp. 407-419
    Mining Sequential Patterns
    Rakesh Agrawal
    ICDE (1995), pp. 3-14
    Fast Algorithms for Mining Association Rules in Large Databases
    Rakesh Agrawal
    VLDB (1994), pp. 487-499
    Quest: A Project on Database Mining
    Rakesh Agrawal
    Michael J. Carey
    Christos Faloutsos
    Sakti P. Ghosh
    Maurice A. W. Houtsma
    Tomasz Imielinski
    Balakrishna R. Iyer
    A. Mahboob
    H. Miranda
    Arun N. Swami
    SIGMOD Conference (1994), pp. 514