@inproceedings{lorince2014supertagger, author = {Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter}, title = {“Supertagger” Behavior in Building Folksonomies}, year = {2014}, keywords = {analysis, distribution, folksonomy, supertagger, tag, tagging, toRead} }

%0 = inproceedings %A = Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter %D = 2014 %T = “Supertagger” Behavior in Building Folksonomies

Since the rise of collaborative tagging systems on the web, the tag recommendation task - suggesting suitable tags to users of such systems while they add resources to their collection - has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.

@inproceedings{doerfel2013analysis, author = {Doerfel, Stephan and Jäschke, Robert}, title = {An Analysis of Tag-Recommender Evaluation Procedures}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, series = {RecSys '13}, publisher = {ACM}, address = {New York, NY, USA}, year = {2013}, pages = {343--346}, url = {http://doi.acm.org/10.1145/2507157.2507222}, doi = {10.1145/2507157.2507222}, isbn = {978-1-4503-2409-0}, keywords = {2013, BibSonomy, core, evaluation, iteg, itegpub, l3s, myown, recsys, tag}, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert %B = Proceedings of the 7th ACM conference on Recommender systems %C = New York, NY, USA %D = 2013 %I = ACM %T = An Analysis of Tag-Recommender Evaluation Procedures %U = http://doi.acm.org/10.1145/2507157.2507222

Since the rise of collaborative tagging systems on the web, the tag recommendation task - suggesting suitable tags to users of such systems while they add resources to their collection - has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.

@inproceedings{doerfel2013analysis, author = {Doerfel, Stephan and Jäschke, Robert}, title = {An Analysis of Tag-Recommender Evaluation Procedures}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, series = {RecSys '13}, publisher = {ACM}, address = {New York, NY, USA}, year = {2013}, pages = {343--346}, url = {http://doi.acm.org/10.1145/2507157.2507222}, doi = {10.1145/2507157.2507222}, isbn = {978-1-4503-2409-0}, keywords = {2013, BibSonomy, core, evaluation, myown, recsys, tag}, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert %B = Proceedings of the 7th ACM conference on Recommender systems %C = New York, NY, USA %D = 2013 %I = ACM %T = An Analysis of Tag-Recommender Evaluation Procedures %U = http://doi.acm.org/10.1145/2507157.2507222

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {CEUR-WS}, address = {Aachen, Germany}, year = {2013}, volume = {1066}, url = {http://ceur-ws.org/Vol-1066/}, keywords = {2013, everyaware, folksonomy, myown, recommender, sensor, tag}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %C = Aachen, Germany %D = 2013 %I = CEUR-WS %T = Tag Recommendations for SensorFolkSonomies %U = http://ceur-ws.org/Vol-1066/

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {ACM}, year = {2013}, pages = {New York, NY, USA}, note = {accepted for publication}, keywords = {2013, RecSys, everyaware, folksonomy, iteg, itegpub, l3s, myown, recommendation, rsweb, sensor, sitc, tag, widenoise}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %D = 2013 %I = ACM %T = Tag Recommendations for SensorFolkSonomies

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {ACM}, year = {2013}, pages = {New York, NY, USA}, note = {accepted for publication}, keywords = {2013, RecSys, everyaware, folksonomy, myown, recommendation, rsweb, sensor, tag, widenoise}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %D = 2013 %I = ACM %T = Tag Recommendations for SensorFolkSonomies

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

@inproceedings{illig2009comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, content, folksonomy, myown, recommendations, recommender, tag}, abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. } }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.

@inproceedings{illig2009comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, content, folksonomy, info20, itegpub, l3s, myown, recommendations, recommender, tag, tagorapub}, abstract = {Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.
} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

@inproceedings{illig2011comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of content-based Tag Recommendations in Folksonomy Systems}, booktitle = {Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007)}, publisher = {Springer}, year = {2011}, keywords = {2011, content, folksonomy, itegpub, l3s, myown, recommendations, recommender, tag, tagorapub} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007) %D = 2011 %I = Springer %T = A Comparison of content-based Tag Recommendations in Folksonomy Systems

@phdthesis{jschke2011formal, author = {Jäschke, Robert}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, publisher = {IOS Press}, address = {[Amsterdam]}, year = {2011}, pages = {--}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325}, isbn = {9781607507079 1607507072 9783898383325 3898383326}, keywords = {bibsonomy, bookmarking, dissertation, fca, recommender, social, tag, tagging, taggingsurvey} }

%0 = phdthesis %A = Jäschke, Robert %C = [Amsterdam] %D = 2011 %I = IOS Press %T = Formal concept analysis and tag recommendations in collaborative tagging systems %U = http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325

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

@incollection{kubatz2011localrank, author = {Kubatz, Marius and Gedikli, Fatih and Jannach, Dietmar}, title = {LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations}, editor = {Huemer, Christian and Setzer, Thomas}, booktitle = {E-Commerce and Web Technologies}, series = {Lecture Notes in Business Information Processing}, publisher = {Springer Berlin Heidelberg}, year = {2011}, volume = {85}, pages = {258-269}, url = {http://dx.doi.org/10.1007/978-3-642-23014-1_22}, doi = {10.1007/978-3-642-23014-1_22}, isbn = {978-3-642-23013-4}, keywords = {folkrank, leavepostout, localrank, recommender, tag}, 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 } }

%0 = incollection %A = Kubatz, Marius and Gedikli, Fatih and Jannach, Dietmar %B = E-Commerce and Web Technologies %D = 2011 %I = Springer Berlin Heidelberg %T = LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations %U = http://dx.doi.org/10.1007/978-3-642-23014-1_22

In a social network, recommenders are highly demanded since they provide user interests in order to construct user profiles. This user profiles might be valuable to be exploited in business management or marketing, for instance. Basically, a tag recommender provides to users a set keywords that describe certain resources. The existing approaches require exploiting content information or they just provide a set of tags without any kind of preference order. This article proposes TagRanker, a tag recommender based on logistic regression that is free of exploiting content information. In addition, it gives a ranking of certain tags and learns just from the relations among users, resources and tags previously posted avoiding the cost of exploiting the content of the resources. An adequate evaluation measure for this specific kind of ranking is also proposed, since the existing ones just consider the tags as coming from a classification. The experiments on several data sets show that TagRanker can effectively recommend relevant tags outperforming the performance of a benchmark of Tag Recommender Systems.

@article{montas2011tagranker, author = {Montañés, Elena and Ramón Quevedo, José and Díaz, Irene and Cortina, Raquel and Alonso, Pedro and Ranilla, José}, title = {TagRanker: learning to recommend ranked tags}, journal = {Logic Journal of IGPL}, year = {2011}, volume = {19}, number = {2}, pages = {395-404}, url = {http://jigpal.oxfordjournals.org/content/19/2/395.abstract}, doi = {10.1093/jigpal/jzq036}, keywords = {LeavePostOut, recommender, tag, tagranker}, abstract = {In a social network, recommenders are highly demanded since they provide user interests in order to construct user profiles. This user profiles might be valuable to be exploited in business management or marketing, for instance. Basically, a tag recommender provides to users a set keywords that describe certain resources. The existing approaches require exploiting content information or they just provide a set of tags without any kind of preference order. This article proposes TagRanker, a tag recommender based on logistic regression that is free of exploiting content information. In addition, it gives a ranking of certain tags and learns just from the relations among users, resources and tags previously posted avoiding the cost of exploiting the content of the resources. An adequate evaluation measure for this specific kind of ranking is also proposed, since the existing ones just consider the tags as coming from a classification. The experiments on several data sets show that TagRanker can effectively recommend relevant tags outperforming the performance of a benchmark of Tag Recommender Systems.} }

%0 = article %A = Montañés, Elena and Ramón Quevedo, José and Díaz, Irene and Cortina, Raquel and Alonso, Pedro and Ranilla, José %D = 2011 %T = TagRanker: learning to recommend ranked tags %U = http://jigpal.oxfordjournals.org/content/19/2/395.abstract

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

@article{zhang2011tagaware, author = {Zhang, Zi-Ke and Zhou, Tao and Zhang, Yi-Cheng}, title = {Tag-Aware Recommender Systems: A State-of-the-Art Survey}, journal = {Journal of Computer Science and Technology}, publisher = {Springer Boston}, year = {2011}, volume = {26}, number = {5}, pages = {767--777}, url = {http://dx.doi.org/10.1007/s11390-011-0176-1}, doi = {10.1007/s11390-011-0176-1}, issn = {1000-9000}, keywords = {recommender, survey, tag, tagging}, abstract = {In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.} }

%0 = article %A = Zhang, Zi-Ke and Zhou, Tao and Zhang, Yi-Cheng %D = 2011 %I = Springer Boston %T = Tag-Aware Recommender Systems: A State-of-the-Art Survey %U = http://dx.doi.org/10.1007/s11390-011-0176-1

Social annotation systems allow users to annotate resources with personalized tags and to navigate large and complex information spaces without the need to rely on predefined hierarchies. These systems help users organize and share their own resources, as well as discover new ones annotated by other users. Tag recommenders in such systems assist users in finding appropriate tags for resources and help consolidate annotations across all users and resources. But the size and complexity of the data, as well as the inherent noise and inconsistencies in the underlying tag vocabularies, have made the design of effective tag recommenders a challenge. Recent efforts have demonstrated the advantages of integrative models that leverage all three dimensions of a social annotation system: users, resources and tags. Among these approaches are recommendation models based on matrix factorization. But, these models tend to lack scalability and often hide the underlying characteristics, or "information channels" of the data that affect recommendation effectiveness. In this paper we propose a weighted hybrid tag recommender that blends multiple recommendation components drawing separately on complementary dimensions, and evaluate it on six large real-world datasets. In addition, we attempt to quantify the strength of the information channels in these datasets and use these results to explain the performance of the hybrid. We find our approach is not only competitive with the state-of-the-art techniques in terms of accuracy, but also has the added benefits of being scalable to large real world applications, extensible to incorporate a wide range of recommendation techniques, easily updateable, and more scrutable than other leading methods.

@inproceedings{gemmell2010hybrid, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, title = {Hybrid tag recommendation for social annotation systems}, booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management}, series = {CIKM '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {829--838}, url = {http://doi.acm.org/10.1145/1871437.1871543}, doi = {10.1145/1871437.1871543}, isbn = {978-1-4503-0099-5}, keywords = {hybrid, recommendation, tag}, abstract = {Social annotation systems allow users to annotate resources with personalized tags and to navigate large and complex information spaces without the need to rely on predefined hierarchies. These systems help users organize and share their own resources, as well as discover new ones annotated by other users. Tag recommenders in such systems assist users in finding appropriate tags for resources and help consolidate annotations across all users and resources. But the size and complexity of the data, as well as the inherent noise and inconsistencies in the underlying tag vocabularies, have made the design of effective tag recommenders a challenge. Recent efforts have demonstrated the advantages of integrative models that leverage all three dimensions of a social annotation system: users, resources and tags. Among these approaches are recommendation models based on matrix factorization. But, these models tend to lack scalability and often hide the underlying characteristics, or "information channels" of the data that affect recommendation effectiveness. In this paper we propose a weighted hybrid tag recommender that blends multiple recommendation components drawing separately on complementary dimensions, and evaluate it on six large real-world datasets. In addition, we attempt to quantify the strength of the information channels in these datasets and use these results to explain the performance of the hybrid. We find our approach is not only competitive with the state-of-the-art techniques in terms of accuracy, but also has the added benefits of being scalable to large real world applications, extensible to incorporate a wide range of recommendation techniques, easily updateable, and more scrutable than other leading methods.} }

%0 = inproceedings %A = Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin %B = Proceedings of the 19th ACM international conference on Information and knowledge management %C = New York, NY, USA %D = 2010 %I = ACM %T = Hybrid tag recommendation for social annotation systems %U = http://doi.acm.org/10.1145/1871437.1871543

The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender.

@inproceedings{musto2010combining, author = {Musto, Cataldo and Narducci, Fedelucio and Lops, Pasquale and de Gemmis, Marco}, title = {Combining Collaborative and Content-Based Techniques for Tag Recommendation.}, editor = {Buccafurri, Francesco and Semeraro, Giovanni}, booktitle = {E-Commerce and Web Technologies}, series = {Lecture Notes in Business Information Processing}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2010}, volume = {61}, pages = {13--23}, url = {http://dx.doi.org/10.1007/978-3-642-15208-5_2}, doi = {10.1007/978-3-642-15208-5_2}, isbn = {978-3-642-15207-8}, keywords = {collaborative, content, recommender, tag, tagging}, abstract = {The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender. } }

%0 = inproceedings %A = Musto, Cataldo and Narducci, Fedelucio and Lops, Pasquale and de Gemmis, Marco %B = E-Commerce and Web Technologies %C = Berlin/Heidelberg %D = 2010 %I = Springer %T = Combining Collaborative and Content-Based Techniques for Tag Recommendation. %U = http://dx.doi.org/10.1007/978-3-642-15208-5_2

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.

@inproceedings{rendle2010pairwise, author = {Rendle, Steffen and Schmidt-Thieme, Lars}, title = {Pairwise interaction tensor factorization for personalized tag recommendation}, booktitle = {Proceedings of the third ACM international conference on Web search and data mining}, series = {WSDM '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {81--90}, url = {http://doi.acm.org/10.1145/1718487.1718498}, doi = {10.1145/1718487.1718498}, isbn = {978-1-60558-889-6}, keywords = {factorization, pitf, recommender, tag, tensor}, abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.} }

%0 = inproceedings %A = Rendle, Steffen and Schmidt-Thieme, Lars %B = Proceedings of the third ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2010 %I = ACM %T = Pairwise interaction tensor factorization for personalized tag recommendation %U = http://doi.acm.org/10.1145/1718487.1718498

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.

@inproceedings{rendle2010pairwise, author = {Rendle, Steffen and Schmidt-Thieme, Lars}, title = {Pairwise interaction tensor factorization for personalized tag recommendation}, booktitle = {Proceedings of the third ACM international conference on Web search and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {81--90}, url = {http://doi.acm.org/10.1145/1718487.1718498}, doi = {10.1145/1718487.1718498}, isbn = {978-1-60558-889-6}, keywords = {collaborative, factorization, folksonomy, personalization, recommender, tag, tagging, tensor}, abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.} }

%0 = inproceedings %A = Rendle, Steffen and Schmidt-Thieme, Lars %B = Proceedings of the third ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2010 %I = ACM %T = Pairwise interaction tensor factorization for personalized tag recommendation %U = http://doi.acm.org/10.1145/1718487.1718498

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

@inproceedings{cattuto2008semantica, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}, editor = {Sheth, Amit P. and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy W. and Thirunarayan, Krishnaprasad}, booktitle = {The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008}, series = {LNAI}, publisher = {Springer}, address = {Heidelberg}, year = {2008}, volume = {5318}, pages = {615--631}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf}, doi = {http://dx.doi.org/10.1007/978-3-540-88564-1_39}, keywords = {2008, grounding, iswc2008, itegpub, methods_concepthierarchy, methods_concepts, myown, ol_web2.0, relatedness, semantic, semantic_relatedness, similarity, sw, tag, tagging, tagorapub}, abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.} }

%0 = inproceedings %A = Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008 %C = Heidelberg %D = 2008 %I = Springer %T = Semantic Grounding of Tag Relatedness in Social Bookmarking Systems %U = http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
er-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

@article{jaeschke2008tag, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Social Bookmarking Systems}, editor = {Giunchiglia, Enrico}, journal = {AI Communications}, publisher = {IOS Press}, address = {Amsterdam}, year = {2008}, volume = {21}, number = {4}, pages = {231-247}, url = {http://dx.doi.org/10.3233/AIC-2008-0438}, doi = {10.3233/AIC-2008-0438}, issn = {0921-7126}, keywords = {2.0, 2008, Recommendations, bookmarking, itegpub, logsonomies, myown, recommendations, recommender, social, systems, tag, tagorapub, tags, web, web2.0, web20}, abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. } }

%0 = article %A = Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %C = Amsterdam %D = 2008 %I = IOS Press %T = Tag Recommendations in Social Bookmarking Systems %U = http://dx.doi.org/10.3233/AIC-2008-0438

he weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.

@article{sinclair2008folksonomy, author = {Sinclair, James and Cardew-Hall, Michael}, title = {The folksonomy tag cloud: when is it useful?}, journal = {Journal of Information Science}, year = {2008}, volume = {34}, number = {1}, pages = {15-29}, url = {http://jis.sagepub.com/content/34/1/15.abstract}, doi = {10.1177/0165551506078083}, keywords = {cloud, tag, useful, weblog}, abstract = { The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset. } }

%0 = article %A = Sinclair, James and Cardew-Hall, Michael %D = 2008 %T = The folksonomy tag cloud: when is it useful? %U = http://jis.sagepub.com/content/34/1/15.abstract