Print Email Facebook Twitter Personalization in professional academic search 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 & InformationMNS - Media & Network ServicesTS - Technical SciencesNetworked InformationInformaticsIndustrial InnovationModeling languagesProfessional aspectsSearch enginesAcademic searchBack-ground knowledgeData collectionLanguage modellingLarge corporaPersonalizationsEducation 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 Files To receive the publication files, please send an e-mail request to TNO Library.