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Ismail Sengor Altingovde
Researcher at Middle East Technical University
Publications - 81
Citations - 1167
Ismail Sengor Altingovde is an academic researcher from Middle East Technical University. The author has contributed to research in topics: Web search query & Web query classification. The author has an hindex of 18, co-authored 79 publications receiving 1039 citations. Previous affiliations of Ismail Sengor Altingovde include Bilkent University.
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Neural information retrieval: at the end of the early years
Kezban Dilek Onal,Kezban Dilek Onal,Ye Zhang,Ismail Sengor Altingovde,Md. Mustafizur Rahman,Pinar Karagoz,Alexander Braylan,Brandon Dang,Heng-Lu Chang,Henna Kim,Quinten McNamara,Aaron Angert,Edward Banner,Vivek Khetan,Tyler McDonnell,An Thanh Nguyen,Dan Xu,Byron C. Wallace,Maarten de Rijke,Matthew Lease +19 more
TL;DR: The successes of neural IR thus far are highlighted, obstacles to its wider adoption are cataloged, and potentially promising directions for future research are suggested.
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Cost-Aware Strategies for Query Result Caching in Web Search Engines
TL;DR: Simulation results using two large Web crawl datasets and a real query log reveal that the proposed approach improves overall system performance in terms of the average query execution time.
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Analyzing and Mining Comments and Comment Ratings on the Social Web
TL;DR: An in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News, which explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior.
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Exploiting interclass rules for focused crawling
TL;DR: A rule-based Web-crawling approach that uses linkage statistics among topics to improve a baseline focused crawler's harvest rate and coverage and enhances the baseline crawler by supporting tunneling.
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Efficiency and effectiveness of query processing in cluster-based retrieval
TL;DR: This study provides CBR efficiency and effectiveness experiments using the largest corpus in an environment that employs no user interaction or user behavior assumption for clustering and confirms that the approach is scalable and system performance improves with increasing database size.