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Can readers understand their profiles: a study of user profiling for information filtering
RISE, Swedish ICT, SICS.
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0003-3909-6751
2001 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The aim in information filtering is to provide users with a personalised selection of information, based on a description of their interest profile. These can be automatically generated or set by users. We can expect that users sometimes will want to be able to review their profiles even if they are system generated. We present a study of the effects of combining automatic profiling with explicit user involvement. Firstly, we wanted to explore if a machine-learned profile would benefit from being based on an initial explicit user profile. Secondly, we tested if profiles that provided better filtering also were better liked by users. Finally, we tested if users could make improvements to machine-learned profiles. We found that the initial set-up of a personal profile was effective, and yielded performance improvements even after feedback training. However, the study showed no correlation between users ratings of profiles and their filtering performance. Neither were there any conclusive evidence that users could improve on the system-generated profiles.

Place, publisher, year, edition, pages
2001, 1.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-22619OAI: oai:DiVA.org:ri-22619DiVA: diva2:1042184
Conference
34th Annual Hawaii International Conference on System Sciences (HICSS-34): Track 4, 3-6 Jan 2001, Maui, Hawaii, USA
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2017-08-04Bibliographically approved

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http://csdl2.computer.org/comp/proceedings/hicss/2001/0981/04/09814012.pdf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Language
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Output format
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