The tripartite graph is one of the commonest topological structures in social
tagging systems such as Delicious, which has three types of nodes (i.e., users,
URLs and tags). Traditional recommender systems developed based on
collaborative filtering for the social tagging systems bring very high demands
on CPU time cost. In this paper, to overcome this drawback, we propose a novel
approach that extracts non-overlapping user clusters and corresponding
overlapping item clusters simultaneously through coarse clustering to
accelerate the user-based collaborative filtering and develop a fast
recommendation algorithm for the social tagging systems. The experimental
results show that the proposed approach is able to dramatically reduce the
processing time cost greater than $90\%$ and relatively enhance the accuracy in
comparison with the ordinary user-based collaborative filtering algorithm.
%0 Generic
%1 zhao2015recommendation
%A Zhao, Yao-Dong
%A Cai, Shi-Min
%A Tang, Ming
%A Shang, Ming-Sheng
%D 2015
%K delicious social_tagging tag-based_recommendation
%T A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious
Case
%U http://arxiv.org/abs/1512.08325
%X The tripartite graph is one of the commonest topological structures in social
tagging systems such as Delicious, which has three types of nodes (i.e., users,
URLs and tags). Traditional recommender systems developed based on
collaborative filtering for the social tagging systems bring very high demands
on CPU time cost. In this paper, to overcome this drawback, we propose a novel
approach that extracts non-overlapping user clusters and corresponding
overlapping item clusters simultaneously through coarse clustering to
accelerate the user-based collaborative filtering and develop a fast
recommendation algorithm for the social tagging systems. The experimental
results show that the proposed approach is able to dramatically reduce the
processing time cost greater than $90\%$ and relatively enhance the accuracy in
comparison with the ordinary user-based collaborative filtering algorithm.
@misc{zhao2015recommendation,
abstract = {The tripartite graph is one of the commonest topological structures in social
tagging systems such as Delicious, which has three types of nodes (i.e., users,
URLs and tags). Traditional recommender systems developed based on
collaborative filtering for the social tagging systems bring very high demands
on CPU time cost. In this paper, to overcome this drawback, we propose a novel
approach that extracts non-overlapping user clusters and corresponding
overlapping item clusters simultaneously through coarse clustering to
accelerate the user-based collaborative filtering and develop a fast
recommendation algorithm for the social tagging systems. The experimental
results show that the proposed approach is able to dramatically reduce the
processing time cost greater than $90\%$ and relatively enhance the accuracy in
comparison with the ordinary user-based collaborative filtering algorithm.},
added-at = {2016-01-03T04:08:47.000+0100},
author = {Zhao, Yao-Dong and Cai, Shi-Min and Tang, Ming and Shang, Ming-Sheng},
biburl = {https://www.bibsonomy.org/bibtex/2cfeb0c1c006a8e138446c8c5a439562b/hangdong},
interhash = {46be58e047597f0fdd9449df9809af03},
intrahash = {cfeb0c1c006a8e138446c8c5a439562b},
keywords = {delicious social_tagging tag-based_recommendation},
note = {cite arxiv:1512.08325Comment: 20 pages, 7 figures},
timestamp = {2016-01-03T04:08:47.000+0100},
title = {A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious
Case},
url = {http://arxiv.org/abs/1512.08325},
year = 2015
}