dc.contributor.advisor | Ulusoy, Özgür | |
dc.contributor.author | Usta, Arif | |
dc.date.accessioned | 2016-05-05T11:32:02Z | |
dc.date.available | 2016-05-05T11:32:02Z | |
dc.date.copyright | 2015-09 | |
dc.date.issued | 2015-09 | |
dc.date.submitted | 2015-09-03 | |
dc.identifier.uri | http://hdl.handle.net/11693/29079 | |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (leaves 80-87). | en_US |
dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2015. | en_US |
dc.description.abstract | Web search is one of the most popular internet activities among users. Due
to high usage of search engines, there are huge data available about history of
user search issues. Using query logs as a source of implicit feedback, researchers
can learn useful patterns about general search behaviors. We employ a detailed
query log analysis provided by a commercial educational vertical search engine.
We compare the results of our query log analysis with the general web search characteristics.
Due to di erence in terms of search behavior between web users and
students, we propose an educational ranking model using learning to rank algorithms
to better re
ect the search habits of the students in the educational domain
to further enhance the search engine performance. We introduce novel features
best suited to the educational domain. We show that our model including educational
features outperforms two baseline models which are the original ranking
of the commercial educational vertical search engine and the model constructed
using the state of the art ranking functions, up to 14% and 11%, respectively.
We also employ di erent learning to rank models for di erent clusters of queries
and the results indicate that having models for each cluster of queries further
enhances the performance of our proposed model. Speci cally, the course of the
query and the grade of the user issuing the query are good sources of feedback
to have a better model in the educational domain. We propose a novel Propagation
Algorithm to be used for queries having lower frequencies where information
derived from query logs is not enough to exploit. We report that our model constructed
using the features generated by our proposed algorithm performs better
for singleton queries compared to both the educational learning to rank model we
introduce and models learned with common features introduced in the literature. | en_US |
dc.description.statementofresponsibility | by Arif Usta. | en_US |
dc.format.extent | xiv, 87 leaves. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Information retrieval | en_US |
dc.subject | Web search | en_US |
dc.subject | Vertical search engine | en_US |
dc.subject | Learning to rank algorithms | en_US |
dc.subject | Educational domain | en_US |
dc.title | Optimization of an educational search engine using learning to rank algorithms | en_US |
dc.title.alternative | Sıralama amaçlı öğrenme algoritmaları kullanarak eğitim tabanlı arama motoru optimizasyonu | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B151169 | |