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 Eren Manavoglu

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Average citations per article22.79
Citation Count319
Publication count14
Publication years2003-2014
Available for download11
Average downloads per article751.55
Downloads (cumulative)8,267
Downloads (12 Months)453
Downloads (6 Weeks)33
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14 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
December 2014 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers: Volume 5 Issue 4, January 2015
Publisher: ACM
Bibliometrics:
Citation Count: 22
Downloads (6 Weeks): 16,   Downloads (12 Months): 173,   Downloads (Overall): 562

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Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and ...
Keywords: distributed learning, feature selection, Display advertising, click prediction, hashing, machine learning

2 published by ACM
August 2012 KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 14
Downloads (6 Weeks): 2,   Downloads (12 Months): 54,   Downloads (Overall): 635

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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) and cost-per-action (CPA). An accurate prediction of the probability that users click on ...
Keywords: flash, multimedia features, display advertising, GMM, click prediction, image, new ads

3 published by ACM
August 2012 ADKDD '12: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 1,   Downloads (12 Months): 18,   Downloads (Overall): 248

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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 publisher. The objective of this work is to investigate ...
Keywords: ad layout optimization, forecasting, display advertising, revenue

4 published by ACM
February 2012 WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 19
Downloads (6 Weeks): 4,   Downloads (12 Months): 35,   Downloads (Overall): 411

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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 conversion rate (CVR). CVRs measure the proportion of certain users who take a ...
Keywords: display advertising, post-click conversion, conversion modeling, non-guaranteed delivery, conversion rate

5
June 2011 Information Retrieval: Volume 14 Issue 3, June 2011
Publisher: Kluwer Academic Publishers
Bibliometrics:
Citation Count: 10

The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many query-ad ...
Keywords: Advertising, Query log mining, Relevance, Clicks

6 published by ACM
July 2010 SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 20
Downloads (6 Weeks): 2,   Downloads (12 Months): 43,   Downloads (Overall): 944

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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 same result might get more clicks when displayed with results of relatively ...
Keywords: click log analysis, advertising, bayesian model, click-through rate, externalities, sponsored search

7 published by ACM
February 2010 WSDM '10: Proceedings of the third ACM international conference on Web search and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 33
Downloads (6 Weeks): 3,   Downloads (12 Months): 37,   Downloads (Overall): 718

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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 using translation models to learn user click propensity from sparse ...
Keywords: advertising, clicks, relevance modeling, translation

8 published by ACM
May 2006 WWW '06: Proceedings of the 15th international conference on World Wide Web
Publisher: ACM
Bibliometrics:
Citation Count: 61
Downloads (6 Weeks): 1,   Downloads (12 Months): 32,   Downloads (Overall): 1,107

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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 measured by communication frequencies while ignoring the semantic information in SNs. In this paper, ...
Keywords: Gibbs sampling, data mining, social network, email, statistical modeling, clustering

9 published by ACM
October 2005 K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 1,   Downloads (12 Months): 10,   Downloads (Overall): 597

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Acknowledgements in research publications, like citations, indicate influential contributions to scientific work; however, large-scale acknowledgement analyses have traditionally been impractical due to the high cost of manual information extraction. In this paper we describe a mixture method for automatically mining acknowledgements from research documents using a combination of a Support ...
Keywords: CiteSeer, acknowledgements, text mining, information extraction

10 published by ACM
March 2005 SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 0,   Downloads (12 Months): 14,   Downloads (Overall): 621

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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 primary feature representation. However, such representation usually comes with high dimensionality and feature sparseness. Word ...
Keywords: feature dimensionality reduction, word clustering

11
November 2004 IEEE Intelligent Systems: Volume 19 Issue 6, November 2004
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 8

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 sequences of document requests. This representation and the maxent approach have several advantages: ...
Keywords: maximum entropy model, sequence modeling, mixture models, recommender systems, recommender systems, maximum entropy model, sequence modeling, mixture models

12
November 2003 ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 24

We present a mixture model based approach for learningindividualized behavior models for the Web users. Weinvestigate the use of maximum entropy and Markov mixturemodels for generating probabilistic behavior models.We first build a global behavior model for the entire populationand then personalize this global model for the existingusers by assigning each ...

13 published by ACM
July 2003 SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 3,   Downloads (12 Months): 25,   Downloads (Overall): 817

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This paper introduces a rule-based, context-dependent word clustering method, with the rules derived from various domain databases and the word text orthographic properties. Besides significant dimensionality reduction, our experiments show that such rule-based word clustering improves by 8 the overall accuracy of extracting bibliographic fields from references, and by 18.32 ...
Keywords: feature dimensionality reduction, word clustering

14
May 2003 JCDL '03: Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 89
Downloads (6 Weeks): 0,   Downloads (12 Months): 12,   Downloads (Overall): 1,607

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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 method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on ...



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