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Conference Paper: Mining User Dwell Time for Personalized Web Search Re-Ranking

TitleMining User Dwell Time for Personalized Web Search Re-Ranking
Authors
Issue Date2011
PublisherAAAI Press/International Joint Conferences on Artificial Intelligence.
Citation
Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Catalonia, Spain, 16–22 July 2011, p. 2367-2372 How to Cite?
AbstractWe propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer concept word level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search engines and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method.
DescriptionSession: Web and Knowledge-Based Information Systems
Persistent Identifierhttp://hdl.handle.net/10722/169318
ISBN

 

DC FieldValueLanguage
dc.contributor.authorXu, Sen_US
dc.contributor.authorJiang, Hen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2012-10-18T08:49:54Z-
dc.date.available2012-10-18T08:49:54Z-
dc.date.issued2011en_US
dc.identifier.citationProceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Catalonia, Spain, 16–22 July 2011, p. 2367-2372en_US
dc.identifier.isbn9781577355168-
dc.identifier.urihttp://hdl.handle.net/10722/169318-
dc.descriptionSession: Web and Knowledge-Based Information Systems-
dc.description.abstractWe propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer concept word level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search engines and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method.-
dc.languageengen_US
dc.publisherAAAI Press/International Joint Conferences on Artificial Intelligence.-
dc.relation.ispartofInternational Joint Conference on Artificial Intelligence (IJCAI 2011)en_US
dc.titleMining User Dwell Time for Personalized Web Search Re-Rankingen_US
dc.typeConference_Paperen_US
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hken_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.5591/978-1-57735-516-8/IJCAI11-394-
dc.identifier.hkuros211553en_US
dc.identifier.spage2367en_US
dc.identifier.epage2372en_US
dc.publisher.placeUnited States-

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