Ramakrishnan Srikant
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Micro-Browsing Models for Search Snippets
International Conference on Data Engineering (ICDE), IEEE (2019), pp. 1904-1909
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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.
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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
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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.
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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
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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.
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Scaling Up All Pairs Similarity Search
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Roberto Bayardo
Yiming Ma
Proc. of the 16th Int'l Conf. on the World Wide Web (2007)
Privacy Preserving OLAP
XPref: a preference language for P3P
Rakesh Agrawal
Jerry Kiernan
Yirong Xu
Computer Networks, vol. 48 (2005), pp. 809-827
An Implementation of P3P Using Database Technology
Enabling Sovereign Information Sharing Using Web Services
Auditing Compliance with a Hippocratic Database
Rakesh Agrawal
Roberto J. Bayardo Jr.
Christos Faloutsos
Jerry Kiernan
Ralf Rantzau
VLDB (2004), pp. 516-527
Order-Preserving Encryption for Numeric Data
Whither Data Mining?
Privacy preserving mining of association rules
Alexandre V. Evfimievski
Rakesh Agrawal
Johannes Gehrke
Inf. Syst., vol. 29 (2004), pp. 343-364
Implementing P3P Using Database Technology
Limiting privacy breaches in privacy preserving data mining
Information Sharing Across Private Databases
An SPath-based preference language for P3P
Searching with Numbers
Mining newsgroups using networks arising from social behavior
Technological Solutions for Protecting Privacy
Scaling mining algorithms to large databases
Paul S. Bradley
Johannes Gehrke
Raghu Ramakrishnan
Commun. ACM, vol. 45 (2002), pp. 38-43
Database Technologies for Electronic Commerce
High-Dimensional Similarity Joins
Kyuseok Shim
Rakesh Agrawal
IEEE Trans. Knowl. Data Eng., vol. 14 (2002), pp. 156-171
Privacy Preserving Data Mining: Challenges and Opportunities
PAKDD (2002), pp. 13
Hippocratic Databases
New Directions in Data Mining
DMKD (2002)
Data Mining Technologies for Digital Libraries and Web Information Systems
ICADL (2002), pp. 75
Mining web logs to improve website organization
Privacy-Preserving Data Mining
Athena: Mining-Based Interactive Management of Text Database
Discovering Predictive Association Rules
Partial Classification Using Association Rules
Range Queries in OLAP Data Cubes
Ching-Tien Ho
Rakesh Agrawal
Nimrod Megiddo
SIGMOD Conference (1997), pp. 73-88
Discovering Trends in Text Databases
High-Dimensional Similarity Joins
Mining Association Rules with Item Constraints
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 Sequential Patterns: Generalizations and Performance Improvements
Mining Quantitative Association Rules in Large Relational Tables
The Quest Data Mining System
Rakesh Agrawal
Manish Mehta
John C. Shafer
Andreas Arning
Toni Bollinger
KDD (1996), pp. 244-249
Mining Generalized Association Rules
Mining Sequential Patterns
Fast Algorithms for Mining Association Rules in Large Databases
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
