Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed.
%0 Conference Paper
%1 citeulike:5142055
%A Godoy, Daniela
%A Amandi, Anal\'ıa
%B 2008 Latin American Web Conference
%D 2008
%I IEEE
%J Latin American Web Conference, 2008. LA-WEB '08.
%K dppaws en recommender tagging user-profile
%P 58--65
%R 10.1109/la-web.2008.15
%T Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems
%U http://dx.doi.org/10.1109/la-web.2008.15
%X Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed.
@inproceedings{citeulike:5142055,
abstract = {{Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Godoy, Daniela and Amandi, Anal\'{\i}a},
biburl = {https://www.bibsonomy.org/bibtex/24b87fa4c7b86bc285bbea65831fd41fc/aho},
booktitle = {2008 Latin American Web Conference},
citeulike-article-id = {5142055},
citeulike-linkout-0 = {http://dx.doi.org/10.1109/la-web.2008.15},
citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4756162},
doi = {10.1109/la-web.2008.15},
interhash = {b50dc3ff165b5540f67d687f9790922a},
intrahash = {4b87fa4c7b86bc285bbea65831fd41fc},
journal = {Latin American Web Conference, 2008. LA-WEB '08.},
keywords = {dppaws en recommender tagging user-profile},
location = {Vitoria, Espiritu, Brazil},
month = oct,
pages = {58--65},
posted-at = {2009-07-14 02:08:24},
priority = {2},
publisher = {IEEE},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems}},
url = {http://dx.doi.org/10.1109/la-web.2008.15},
year = 2008
}