Ismail Sengor Altingovde

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.

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.

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.

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.

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.