NDLI: Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Content Provider	Springer Nature Link
Author	Keyhanipour, Amir Hosein Moshiri, Behzad Oroumchian, Farhad Rahgozar, Maseud Badie, Kambiz
Copyright Year	2016
Abstract	Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.
Starting Page	203
Ending Page	230
Page Count	28
File Format	PDF
ISSN	13892576
Journal	Genetic Programming and Evolvable Machines
Volume Number	17
Issue Number	3
e-ISSN	15737632
Language	English
Publisher	Springer US
Publisher Date	2016-01-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning to rank Click-through data Layered multi-population genetic programming Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Electrical Engineering Programming Techniques Programming Languages, Compilers, Interpreters Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science Applications Software Hardware and Architecture

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