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Displaying 1-9 out of 9 total
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
By Dmitry Pavlov, Eren Manavoglu, David M. Pennock, C. Lee Giles
Issue Date:November 2004
The authors describe a novel maximum-entropy (maxent) approach for generating online recommendations as a user navigates through a collection of documents. They show how to handle high-dimensional sparse data and represent it as a collection of ordered seq...
Automatic Document Metadata Extraction Using Support Vector Machines
Digital Libraries, Joint Conference on
By Hui Han, C. Lee Giles, Eren Manavoglu, Hongyuan Zha, Zhenyue Zhang, Edward A. Fox
Issue Date:May 2003
Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based metho...
Dynamic ad layout revenue optimization for display advertising
Found in: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy (ADKDD '12)
By Eren Manavoglu, Haibin Cheng, Jianchang Mao, Ruofei Zhang, Ying Cui
Issue Date:August 2012
Display advertising has been growing rapidly in recent years, with revenue generated from display ads placed on spaces allocated on publisher's web pages. Traditionally, the design and layout of ad spaces on a web page are predetermined and fixed for the p...
Multimedia features for click prediction of new ads in display advertising
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Eren Manavoglu, Haibin Cheng, Javad Azimi, Roelof van Zwol, Ruofei Zhang, Vidhya Navalpakkam, Yang Zhou
Issue Date:August 2012
Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC)...
Post-click conversion modeling and analysis for non-guaranteed delivery display advertising
Found in: Proceedings of the fifth ACM international conference on Web search and data mining (WSDM '12)
By Romer Rosales, Eren Manavoglu, Haibin Cheng
Issue Date:February 2012
In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the c...
Temporal click model for sponsored search
Found in: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10)
By Eren Manavoglu, Erick Cantu-Paz, Wanhong Xu
Issue Date:July 2010
Previous studies on search engine click modeling have identified two presentation factors that affect users' behavior: (1) position bias: the same result will get a different number of clicks when displayed in different positions and (2) externalities: the...
Improving ad relevance in sponsored search
Found in: Proceedings of the third ACM international conference on Web search and data mining (WSDM '10)
By Chirs Leggetter, Dustin Hillard, Eren Manavoglu, Hema Raghavan, Stefan Schroedl
Issue Date:February 2010
We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach ...
Probabilistic models for discovering e-communities
Found in: Proceedings of the 15th international conference on World Wide Web (WWW '06)
By C. Lee Giles, Ding Zhou, Eren Manavoglu, Hongyuan Zha, Jia Li
Issue Date:May 2006
The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology ...
Rule-based word clustering for document metadata extraction
Found in: Proceedings of the 2005 ACM symposium on Applied computing (SAC '05)
By C. Lee Giles, Eren Manavoglu, Hongyuan Zha, Hui Han, Kostas Tsioutsiouliklis, Xiangmin Zhang
Issue Date:March 2005
Text classification is still an important problem for unlabeled text; CiteSeer, a computer science document search engine, uses automatic text classification methods for document indexing. Text classification uses a document's original text words as the pr...
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