Personalized retrieval in social bookmarking
|Title:||Personalized retrieval in social bookmarking||Authors:||Bateman, Scott
Muller, Michael J.
|Permanent link:||http://hdl.handle.net/10197/1249||Date:||2009||Abstract:||Users of social bookmarking systems take advantage of pivot browsing, an interaction technique allowing them to easily refine lists of bookmarks through the selection of filter terms. However, social bookmarking systems use onesizefitsall ranking metrics to order refined lists. These generic rankings ignore past user interactions that may be useful in determining the relevance of bookmarks. In this work we describe a personalized ordering algorithm that leverages the fact that refinding, rather than discovery (finding a bookmark for the first time), makes up the majority of bookmark accesses. The algorithm examines useraccess histories and promotes bookmarks that a user has previously visited. We investigate the potential of our algorithm using interaction logs from an enterprise social bookmarking system, the results show that our personalized algorithm would lead to improved bookmark rankings.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Association for Computing Machinery||Copyright (published version):||2009 by the Association for Computing Machinery (ACM)||Keywords:||Social bookmarking;Personalization;Information retrieval||Subject LCSH:||Information retrieval
Recommender systems (Information filtering)
Online social networks
|DOI:||10.1145/1531674.1531688||Language:||en||Status of Item:||Peer reviewed||Is part of:||Group '09 : proceedings of the 2009 ACM SIGCHI international conference on supporting group work||Conference Details:||Presented at the 2009 ACM SIGCHI international conference on supporting group work, May 10–13 2009, Sanibel Island, Florida|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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