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.
%0 Conference Paper
%1 citeulike:9520401
%A Lee, Danielle H.
%A Brusilovsky, Peter
%B Proceedings of the Fourth ACM Conference on Recommender Systems
%C New York, NY, USA
%D 2010
%I ACM
%K group recommender tagging
%P 221--224
%R 10.1145/1864708.1864752
%T Using Self-defined Group Activities for Improvingrecommendations in Collaborative Tagging Systems
%U http://dx.doi.org/10.1145/1864708.1864752
%X 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.
%@ 978-1-60558-906-0
@inproceedings{citeulike:9520401,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Lee, Danielle H. and Brusilovsky, Peter},
biburl = {https://www.bibsonomy.org/bibtex/229ae07766e48442e82ec582b050ecb89/aho},
booktitle = {Proceedings of the Fourth ACM Conference on Recommender Systems},
citeulike-article-id = {9520401},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1864708.1864752},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/1864708.1864752},
doi = {10.1145/1864708.1864752},
interhash = {6fd1cbcfd94da174c910d9144467372a},
intrahash = {29ae07766e48442e82ec582b050ecb89},
isbn = {978-1-60558-906-0},
keywords = {group recommender tagging},
location = {Barcelona, Spain},
pages = {221--224},
posted-at = {2011-07-07 03:15:57},
priority = {0},
publisher = {ACM},
series = {RecSys '10},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Using Self-defined Group Activities for Improvingrecommendations in Collaborative Tagging Systems}},
url = {http://dx.doi.org/10.1145/1864708.1864752},
year = 2010
}