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Matching Task Profiles And User Needs In Personalized Web Search

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Matching Task Profiles and User Needs in Personalized Web Search Julia Luxenburger, Shady Elbassuoni, Gerhard Weikum CIKM ’08 Advisor: Chia-Hui Chang Student: Teng-Kai Fan Date: 2009-10-13
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object]
Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalization Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
[object Object],[object Object],Session : user's timing as well as the relatedness of subsequent user's actions. Profile : query chains (subsequently posed queries), result sets, clicked result pages, the clickstream of subsequently visited web pages.
Selective Personalization Strategy ,[object Object],[object Object],[object Object],[object Object]
Selective Personalization Strategy cont. ,[object Object],[object Object],[object Object],[object Object]
Means of Personalization ,[object Object],[object Object]
Means of Personalization cont. ,[object Object],[object Object],[object Object],[object Object]
Task Language Model ,[object Object],[object Object],[object Object],[object Object]
Query language model ,[object Object],[object Object]
Query language model cont. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Query language model cont. ,[object Object],[object Object],[object Object]
Facet Language Model ,[object Object]
Experimental Setup ,[object Object]
Experimental Setup cont. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Setup cont. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation results with re-ranking ,[object Object],[object Object],[object Object],[object Object]
Query Expanding Evaluation v ( w ) < τ v ( w ) < δ
Correlating KL-divergence with performance gains ,[object Object],[object Object]
Parameterizing the personalization framework
Efficiency ,[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object]

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