Open AccessBandit algorithms in information retrieval evaluation and ranking
Author(s) -
Sinyinda Muwanei,
Hoo Wai Lam,
Sri Devi Ravana,
Douglas Kunda
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1339/1/012005
Subject(s) - ranking (information retrieval) , computer science , domain (mathematical analysis) , learning to rank , information retrieval , ranking svm , artificial intelligence , machine learning , algorithm , data mining , mathematics , mathematical analysis
Bandit algorithms have been widely used in many application areas including information retrieval evaluation and ranking. This is largely due to their exceptional performance. The aim of this study is to examine the overall published studies in terms of trends that shape the use of bandit algorithms in the evaluation and ranking of information retrieval systems. This study also seeks to classify the bandit algorithms used in the research domain. In totality the evaluation metrics, datasets, contribution facets of primary studies as well as the bandit categories are discussed.