Collaborative tagging applications allow users to annotate online
resources. The result is a complex tapestry of interrelated users, resources
and tags often called a folksonomy. Folksonomies present
an attractive target for data mining applications such as tag recommenders.
A challenge of tag recommendation remains the adaptation
of traditional recommendation techniques originally designed
to work with two dimensional data. To date the most successful
recommenders have been graph based approaches which explicitly
connects all three components of the folksonomy.
In this paper we speculate that graph based tag recommendation
can be improved by coupling it with item-based collaborative
filtering. We motive this hypothesis with a discussion of informational
channels in folksonomies and provide a theoretical explanation
of the additive potential for item-based collaborative filtering.
We then provided experimental results on hybrid tag recommenders
built from graph models and other techniques based on popularity,
user-based collaborative filtering and item-based collaborative filtering.
We demonstrate that a hybrid recommender built from a graph
based model and item-based collaborative filtering outperforms its
constituent recommenders. furthermore the inability of the other
recommenders to improve upon the graph-based approach suggests
that they offer information already included in the graph based
model. These results confirm our conjecture. We provide extensive
evaluation of the hybrids using data collected from three real
world collaborative tagging applications.
%0 Conference Paper
%1 gemmell2009improving
%A Gemmell, Jonathan
%A Schimoler, Thomas R.
%A Christiansen, Laura
%A Mobasher, Bamshad
%B ACM RecSys'09 Workshop on Recommender Systems and the Social Web
%D 2009
%E Jannach, Dietmar
%E Geyer, Werner
%E Freyne, Jill
%E Anand, Sarabjot Singh
%E Dugan, Casey
%E Mobasher, Bamshad
%E Kobsa, Alfred
%K collaborative filtering folkrank hybrid
%P 17--24
%T Improving Folkrank With Item-Based Collaborative Filtering
%U http://ceur-ws.org/Vol-532/paper3.pdf
%V 532
%X Collaborative tagging applications allow users to annotate online
resources. The result is a complex tapestry of interrelated users, resources
and tags often called a folksonomy. Folksonomies present
an attractive target for data mining applications such as tag recommenders.
A challenge of tag recommendation remains the adaptation
of traditional recommendation techniques originally designed
to work with two dimensional data. To date the most successful
recommenders have been graph based approaches which explicitly
connects all three components of the folksonomy.
In this paper we speculate that graph based tag recommendation
can be improved by coupling it with item-based collaborative
filtering. We motive this hypothesis with a discussion of informational
channels in folksonomies and provide a theoretical explanation
of the additive potential for item-based collaborative filtering.
We then provided experimental results on hybrid tag recommenders
built from graph models and other techniques based on popularity,
user-based collaborative filtering and item-based collaborative filtering.
We demonstrate that a hybrid recommender built from a graph
based model and item-based collaborative filtering outperforms its
constituent recommenders. furthermore the inability of the other
recommenders to improve upon the graph-based approach suggests
that they offer information already included in the graph based
model. These results confirm our conjecture. We provide extensive
evaluation of the hybrids using data collected from three real
world collaborative tagging applications.
@inproceedings{gemmell2009improving,
abstract = {Collaborative tagging applications allow users to annotate online
resources. The result is a complex tapestry of interrelated users, resources
and tags often called a folksonomy. Folksonomies present
an attractive target for data mining applications such as tag recommenders.
A challenge of tag recommendation remains the adaptation
of traditional recommendation techniques originally designed
to work with two dimensional data. To date the most successful
recommenders have been graph based approaches which explicitly
connects all three components of the folksonomy.
In this paper we speculate that graph based tag recommendation
can be improved by coupling it with item-based collaborative
filtering. We motive this hypothesis with a discussion of informational
channels in folksonomies and provide a theoretical explanation
of the additive potential for item-based collaborative filtering.
We then provided experimental results on hybrid tag recommenders
built from graph models and other techniques based on popularity,
user-based collaborative filtering and item-based collaborative filtering.
We demonstrate that a hybrid recommender built from a graph
based model and item-based collaborative filtering outperforms its
constituent recommenders. furthermore the inability of the other
recommenders to improve upon the graph-based approach suggests
that they offer information already included in the graph based
model. These results confirm our conjecture. We provide extensive
evaluation of the hybrids using data collected from three real
world collaborative tagging applications.},
added-at = {2015-07-25T16:03:19.000+0200},
author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad},
biburl = {https://www.bibsonomy.org/bibtex/26b1ff3b7b691b84288fb7122968134c4/sdo},
booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web},
editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred},
interhash = {0900f921d87c5ee19a4ed2c70e5a71df},
intrahash = {6b1ff3b7b691b84288fb7122968134c4},
issn = {1613-0073},
keywords = {collaborative filtering folkrank hybrid},
month = oct,
pages = {17--24},
series = {CEUR-WS.org},
timestamp = {2015-07-25T16:03:19.000+0200},
title = {Improving Folkrank With Item-Based Collaborative Filtering},
url = {http://ceur-ws.org/Vol-532/paper3.pdf},
volume = 532,
year = 2009
}