Title
Personalization in professional academic search
Author
Verberne, S.
Sappelli, M.
Sørensen, D.R.
Kraaij, W.
Contributor
Lupu, M. (editor)
Fuhr, N. (editor)
Larsen, B. (editor)
Strindberg, H. (editor)
Hanbury, A. (editor)
Salampasis, M. (editor)
Publication year
2013
Abstract
In this paper, we investigated how academic search can profit from personalization by incorporating query history and background knowledge in the ranking of the results. We implemented both techniques in a language modelling framework, using the Indri search engine. For our experiments, we used the iSearch data collection, a large corpus of documents from the physics domain together with 65 search topics from scientists and students. We found that it is possible to improve academic search by taking into account query history. However, we have not been able to prove that terms extracted from the user's background data can improve academic search.
Subject
Communication & Information
MNS - Media & Network Services
TS - Technical Sciences
Networked Information
Informatics
Industrial Innovation
Modeling languages
Professional aspects
Search engines
Academic search
Back-ground knowledge
Data collection
Language modelling
Large corpora
Personalizations
Education
To reference this document use:
http://resolver.tudelft.nl/uuid:d7e2e41e-e334-4e46-b786-9e2620a4d686
TNO identifier
523281
Publisher
CEUR-WS
Source
Workshop on Integrating IR Technologies for Professional Search, IRPS 2013; 24 March 2013, Moscow, Russia, 76-83
Series
CEUR Workshop Proceedings
Document type
conference paper