Semi-active learning to rank algorithms for document retrieval
by Faiza Dammak; Hager Kammoun; Sawssen Ben Hmid; Abdelmajid Ben Hamadou
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 10, No. 3/4, 2017

Abstract: Recently, several search engine applications are using learning to rank technologies to train their ranking models whose performance is strongly affected by labelled examples' number in the training set. Since these labels might be costly to acquire as labelling is usually scarce and expensive to get, active learning and semi-supervised learning technologies aim to reduce manual labelling workload. In this paper, we propose two inductive learning to rank strategies of alternatives that combine active and semi-supervised learning to assign the relevance scores to an unlabeled set of document-query pairs, using selectively sampled and automatically labelled data. These propositions enable the exploitation of all collected data and the avoidance of some problems caused by employing only active or semi-supervised learning. We showed through different ranking measures that the algorithms proposed yielded into competitive results compared to some other semi-supervised and active ranking algorithms on collections from the standard benchmark Letor.

Online publication date: Wed, 11-Oct-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com