On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular
Description
LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations - Springer
%0 Book Section
%1 kubatz2011localrank
%A Kubatz, Marius
%A Gedikli, Fatih
%A Jannach, Dietmar
%B E-Commerce and Web Technologies
%D 2011
%E Huemer, Christian
%E Setzer, Thomas
%I Springer Berlin Heidelberg
%K folkrank leavepostout local localrank neighborhood recommender tag
%P 258-269
%R 10.1007/978-3-642-23014-1_22
%T LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations
%U http://dx.doi.org/10.1007/978-3-642-23014-1_22
%V 85
%X On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular
%@ 978-3-642-23013-4
@incollection{kubatz2011localrank,
abstract = {On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular },
added-at = {2014-06-17T17:48:58.000+0200},
author = {Kubatz, Marius and Gedikli, Fatih and Jannach, Dietmar},
biburl = {https://www.bibsonomy.org/bibtex/2f62135043913269240b8e7105c418214/sdo},
booktitle = {E-Commerce and Web Technologies},
description = {LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations - Springer},
doi = {10.1007/978-3-642-23014-1_22},
editor = {Huemer, Christian and Setzer, Thomas},
interhash = {19a8194d47a5f6722a563a3689606440},
intrahash = {f62135043913269240b8e7105c418214},
isbn = {978-3-642-23013-4},
keywords = {folkrank leavepostout local localrank neighborhood recommender tag},
pages = {258-269},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Business Information Processing},
timestamp = {2015-07-25T14:39:53.000+0200},
title = {LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations},
url = {http://dx.doi.org/10.1007/978-3-642-23014-1_22},
volume = 85,
year = 2011
}