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 Muhammad Ibrahim

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Average citations per article0.50
Citation Count1
Publication count2
Publication years2016-2017
Available for download2
Average downloads per article124.50
Downloads (cumulative)249
Downloads (12 Months)179
Downloads (6 Weeks)22
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1 published by ACM
August 2017 ACM SIGIR Forum: Volume 51 Issue 1, June 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5,   Downloads (12 Months): 12,   Downloads (Overall): 12

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For a query submitted by a user, the goal of an information retrieval system is to return a list of documents which are highly relevant with respect to that query. Traditionally different scoring methods, ranging from simple heuristic models to probabilistic models, have been used for this task. Recently researchers ...

2 published by ACM
August 2016 ACM Transactions on Information Systems (TOIS): Volume 34 Issue 4, September 2016
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 17,   Downloads (12 Months): 167,   Downloads (Overall): 237

Full text available: PDFPDF
Current random-forest (RF)-based learning-to-rank (LtR) algorithms use a classification or regression framework to solve the ranking problem in a pointwise manner. The success of this simple yet effective approach coupled with the inherent parallelizability of the learning algorithm makes it a strong candidate for widespread adoption. In this article, we ...
Keywords: splitting criterion, computational complexity, Learning-to-rank, objective function, evaluation metrics, random forest



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