Harnessing the power of Social Bookmarking for improving tag-based
Recommendations
G. Pitsilis, and W. Wang. (2014)cite arxiv:1410.5072Comment: 28 pages, 10 figures, 4 tables.
Abstract
Social bookmarking and tagging has emerged a new era in user collaboration.
Collaborative Tagging allows users to annotate content of their liking, which
via the appropriate algorithms can render useful for the provision of product
recommendations. It is the case today for tag-based algorithms to work
complementary to rating-based recommendation mechanisms to predict the user
liking to various products. In this paper we propose an alternative algorithm
for computing personalized recommendations of products, that uses exclusively
the tags provided by the users. Our approach is based on the idea of using the
semantic similarity of the user-provided tags for clustering them into groups
of similar meaning. Afterwards, some measurable characteristics of users'
Annotation Competency are combined with other metrics, such as user similarity,
for computing predictions. The evaluation on data used from a real-world
collaborative tagging system, citeUlike, confirmed that our approach
outperforms the baseline Vector Space model, as well as other state of the art
algorithms, predicting the user liking more accurately.
Description
Harnessing the power of Social Bookmarking for improving tag-based
Recommendations
%0 Generic
%1 pitsilis2014harnessing
%A Pitsilis, Georgios
%A Wang, Wei
%D 2014
%K tag
%T Harnessing the power of Social Bookmarking for improving tag-based
Recommendations
%U http://arxiv.org/abs/1410.5072
%X Social bookmarking and tagging has emerged a new era in user collaboration.
Collaborative Tagging allows users to annotate content of their liking, which
via the appropriate algorithms can render useful for the provision of product
recommendations. It is the case today for tag-based algorithms to work
complementary to rating-based recommendation mechanisms to predict the user
liking to various products. In this paper we propose an alternative algorithm
for computing personalized recommendations of products, that uses exclusively
the tags provided by the users. Our approach is based on the idea of using the
semantic similarity of the user-provided tags for clustering them into groups
of similar meaning. Afterwards, some measurable characteristics of users'
Annotation Competency are combined with other metrics, such as user similarity,
for computing predictions. The evaluation on data used from a real-world
collaborative tagging system, citeUlike, confirmed that our approach
outperforms the baseline Vector Space model, as well as other state of the art
algorithms, predicting the user liking more accurately.
@misc{pitsilis2014harnessing,
abstract = {Social bookmarking and tagging has emerged a new era in user collaboration.
Collaborative Tagging allows users to annotate content of their liking, which
via the appropriate algorithms can render useful for the provision of product
recommendations. It is the case today for tag-based algorithms to work
complementary to rating-based recommendation mechanisms to predict the user
liking to various products. In this paper we propose an alternative algorithm
for computing personalized recommendations of products, that uses exclusively
the tags provided by the users. Our approach is based on the idea of using the
semantic similarity of the user-provided tags for clustering them into groups
of similar meaning. Afterwards, some measurable characteristics of users'
Annotation Competency are combined with other metrics, such as user similarity,
for computing predictions. The evaluation on data used from a real-world
collaborative tagging system, citeUlike, confirmed that our approach
outperforms the baseline Vector Space model, as well as other state of the art
algorithms, predicting the user liking more accurately.},
added-at = {2017-03-12T17:06:32.000+0100},
author = {Pitsilis, Georgios and Wang, Wei},
biburl = {https://www.bibsonomy.org/bibtex/2242a2eb7d0bcb137517b2e680204c2bd/arianepatrizia},
description = {Harnessing the power of Social Bookmarking for improving tag-based
Recommendations},
interhash = {5722e38bcb0630d325b1a61f7a54a81a},
intrahash = {242a2eb7d0bcb137517b2e680204c2bd},
keywords = {tag},
note = {cite arxiv:1410.5072Comment: 28 pages, 10 figures, 4 tables},
timestamp = {2017-03-12T17:06:32.000+0100},
title = {Harnessing the power of Social Bookmarking for improving tag-based
Recommendations},
url = {http://arxiv.org/abs/1410.5072},
year = 2014
}