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Using self-defined group activities for improving recommendations in collaborative tagging systems

UNSPECIFIED (2010) Using self-defined group activities for improving recommendations in collaborative tagging systems. In: UNSPECIFIED.

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Abstract

This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study. Copyright 2010 ACM.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Date: 15 December 2010
Date Type: Publication
Journal or Publication Title: RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems
Page Range: 221 - 224
Event Type: Conference
DOI or Unique Handle: 10.1145/1864708.1864752
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781450304429
Related URLs:
Other ID: DOI: 10.1145/1864708.1864752, ISBN: 978-1-60558-906-0
Date Deposited: 06 Jul 2011 19:27
Last Modified: 07 Jan 2019 15:55
URI: http://d-scholarship.pitt.edu/id/eprint/5974

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