Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics
A. Byde, H. Wan, und S. Cayzer. Proceedings of the International Conference on Weblogs and Social
Media, (März 2007)
Zusammenfassung
This short paper describes a novel technique for generating personalized
tag recommendations for users of social book- marking sites such
as del.icio.us. Existing techniques recom- mend tags on the basis
of their popularity among the group of all users; on the basis of
recent use; or on the basis of simple heuristics to extract keywords
from the url being tagged. Our method is designed to complement these
approaches, and is based on recommending tags from urls that are
similar to the one in question, according to two distinct similarity
metrics, whose principal utility covers complementary cases.
%0 Conference Paper
%1 byde2007personalized
%A Byde, Andrew
%A Wan, Hui
%A Cayzer, Steve
%B Proceedings of the International Conference on Weblogs and Social
Media
%D 2007
%K content recommender similarity tag tagging
%T Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics
%U http://www.icwsm.org/papers/paper47.html
%X This short paper describes a novel technique for generating personalized
tag recommendations for users of social book- marking sites such
as del.icio.us. Existing techniques recom- mend tags on the basis
of their popularity among the group of all users; on the basis of
recent use; or on the basis of simple heuristics to extract keywords
from the url being tagged. Our method is designed to complement these
approaches, and is based on recommending tags from urls that are
similar to the one in question, according to two distinct similarity
metrics, whose principal utility covers complementary cases.
@inproceedings{byde2007personalized,
abstract = {This short paper describes a novel technique for generating personalized
tag recommendations for users of social book- marking sites such
as del.icio.us. Existing techniques recom- mend tags on the basis
of their popularity among the group of all users; on the basis of
recent use; or on the basis of simple heuristics to extract keywords
from the url being tagged. Our method is designed to complement these
approaches, and is based on recommending tags from urls that are
similar to the one in question, according to two distinct similarity
metrics, whose principal utility covers complementary cases.},
added-at = {2008-10-16T17:21:28.000+0200},
author = {Byde, Andrew and Wan, Hui and Cayzer, Steve},
biburl = {https://www.bibsonomy.org/bibtex/2157846898c1c2a65c265a913ebac115a/jaeschke},
booktitle = {Proceedings of the International Conference on Weblogs and Social
Media},
interhash = {38aaca7e5b9c508a5901f4109dabaa69},
intrahash = {157846898c1c2a65c265a913ebac115a},
keywords = {content recommender similarity tag tagging},
month = {March},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics},
url = {http://www.icwsm.org/papers/paper47.html},
year = 2007
}