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Learning to Rank - Feature Engineering Using a Click Model

Voncina, Klemen (2018) Learning to Rank - Feature Engineering Using a Click Model. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Effective ranking in information retrieval is done, in part, by proper feature engineering. This paper explores a comparison between the functions of a click model and a ranking function in information retrieval. It then uses the output of a basic bi-class click model as a feature for training a ranking model. Training both of these different approaches on data from a commercial search engine we find that click model performance improves as the threshold for what is a click becomes more stringent and that using the output of a click model as a feature for ranking performs empirically worse than without this added feature.

Item Type: Thesis (Bachelor's Thesis)
Supervisor:
Supervisor nameSupervisor E mail
Wiering, M.A.M.A.Wiering@rug.nl
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 25 Jul 2018
Last Modified: 27 Jul 2018 12:50
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/18052

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