Research Area:  Machine Learning
Generally, the purpose of learning to rank methods is to combine the results from existing ranking models that within a single ranking function, applied to order the documents as efficiently as possible, improving the quality lists of results returned. However, learning to rank has several limitations namely the creation and size of the labeled database. We have considered the two frameworks of semi-supervised and active learning in order to look for solutions to these problems. We have been interested in semi-supervised, active and semi-active learning to rank algorithms for Document Retrieval (DR) which is a ranking application of alternatives. A good balance between exploration and exploitation has a positive impact on the performance of the learning. Thus, we have focused firstly on two active learning to rank algorithms that use supervised learning and semi-supervised learning as auxiliaries and use an automatic method for the labeling of unlabeled pairs selected. These algorithms are named Semi-Active Learning to Rank: SAL2R and Active-Semi-Supervised Learning to Rank: ASSL2R. We have been particulary interested in providing efficient and effective algorithms to handle a large set of unlabeled data. Second, we have considered improvement of these semi-active SAL2R and ASSL2R algorithms using a multi-pair in the selection step. Our contribution lies particulary in the in depth experimental study of the performance of these algorithms and precisely the influence of certain fixed parameters on the learned ranking function.
Author(s) Name:  Faiza Dammak & Hager Kammoun
Journal name:  Information Retrieval Journal
Publisher name:  Springer
Volume Information:  volume 24, pages 371–399 (2021)
Paper Link:   https://link.springer.com/article/10.1007/s10791-021-09396-2