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Active Learning to Rank Method for Documents Retrieval

Authors:
Faïza Dammak
Imen Gabsi
Hager Kammoun
Abdelmajid Ben Hamadou

Keywords: active learning, learning to rank, boosting ranking algorithms

Abstract:
This paper presents a new active learning to rank algorithm based on boosting for active ranking functions. The main goal of this algorithm is to introduce unlabeled data in the learning process. Since this type of ranking is based on a phase of selection of the most informative examples to label, the proposed algorithm allows the cost of labeling to be reduced. In a first step, the algorithm proposed is going to select at each iteration the most informative query-document pair from unlabeled data using the “Query by Committee” strategy. It is this pair which maximizes the measure of disagreement between a representative committee model chosen randomly and the model generated by the supervised algorithm. In fact, the randomly chosen model is generated from the main labeled set. While the other model is generated from the labeled set which changes in each iteration, by using a supervised ranking algorithm. For the latter, we choose to use three algorithms of boosting: RankBoost belonging to the family of the pairwise approach; AdaRank and LambdaMART belonging to the family of the listwise approach. Our choise is meant to subsequently compare the performance of pairwise and listwise approaches. In a second step, once this pair is selected, it will be added to the labeled set. To evaluate the performance of the active model proposed, we hav carried out an experimental study using the benchmark Letor 4.0 dataset. The obtained results show that the active model has a significant improvement in Normalized Discounted Cumulative Gain and Mean Average Precision.

Pages: 16 to 21

Copyright: Copyright (c) IARIA, 2015

Publication date: June 21, 2015

Published in: conference

ISSN: 2308-3972

ISBN: 978-1-61208-412-1

Location: Brussels, Belgium

Dates: from June 21, 2015 to June 26, 2015