The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.

@incollection{marinho2011social, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, title = {Social Tagging Recommender Systems}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, booktitle = {Recommender Systems Handbook}, publisher = {Springer}, address = {New York}, year = {2011}, pages = {615--644}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, doi = {10.1007/978-0-387-85820-3_19}, isbn = {978-0-387-85820-3}, keywords = {tagging, itegpub, recommender, collaborative, 2011, social, myown, info20}, abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.} }

%0 = incollection %A = Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis %B = Recommender Systems Handbook %C = New York %D = 2011 %I = Springer %T = Social Tagging Recommender Systems %U = http://dx.doi.org/10.1007/978-0-387-85820-3_19

The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.

@incollection{marinho2011social, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, title = {Social Tagging Recommender Systems}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, booktitle = {Recommender Systems Handbook}, publisher = {Springer}, address = {New York}, year = {2011}, pages = {615--644}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, doi = {10.1007/978-0-387-85820-3_19}, isbn = {978-0-387-85820-3}, keywords = {tagging, recommender, collaborative, 2011, social, myown}, abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.} }

%0 = incollection %A = Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis %B = Recommender Systems Handbook %C = New York %D = 2011 %I = Springer %T = Social Tagging Recommender Systems %U = http://dx.doi.org/10.1007/978-0-387-85820-3_19

@incollection{reference/rsh/MarinhoNSJHSS11, author = {Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, title = {Social Tagging Recommender Systems.}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, booktitle = {Recommender Systems Handbook}, publisher = {Springer}, year = {2011}, pages = {615-644}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11}, isbn = {978-0-387-85819-7}, keywords = {tagging, taggingsurvey, recommender, 2011, folksonomy, myown} }

%0 = incollection %A = Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis %B = Recommender Systems Handbook %D = 2011 %I = Springer %T = Social Tagging Recommender Systems. %U = http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11

Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.

@inproceedings{rendle2009learning, author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {727--736}, url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100}, doi = {10.1145/1557019.1557100}, isbn = {978-1-60558-495-9}, keywords = {factorization, ranking, folksonomy, learning, optimal, folkrank, tensor}, abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.} }

%0 = inproceedings %A = Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars %B = KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining %C = New York, NY, USA %D = 2009 %I = ACM %T = Learning optimal ranking with tensor factorization for tag recommendation %U = http://portal.acm.org/citation.cfm?doid=1557019.1557100

Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.

@inproceedings{rendle2009learning, author = {Rendle, Steffen and Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {727--736}, url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100}, doi = {10.1145/1557019.1557100}, isbn = {978-1-60558-495-9}, keywords = {tagging, recommender, tag, ranking, folksonomy, tensor}, abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.} }

%0 = inproceedings %A = Rendle, Steffen and Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars %B = KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining %C = New York, NY, USA %D = 2009 %I = ACM %T = Learning optimal ranking with tensor factorization for tag recommendation %U = http://portal.acm.org/citation.cfm?doid=1557019.1557100

Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.

@inproceedings{1557100, author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {727--736}, url = {http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD}, doi = {http://doi.acm.org/10.1145/1557019.1557100}, isbn = {978-1-60558-495-9}, keywords = {tagging, taggingsurvey, recommender, fast, folksonomy, tensor}, abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.} }

%0 = inproceedings %A = Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars %B = KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining %C = New York, NY, USA %D = 2009 %I = ACM %T = Learning optimal ranking with tensor factorization for tag recommendation %U = http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD

The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows down the full materialization of the SemanticWeb since these systems allow ordinary users to create and share knowledge in a simple, cheap, and scalable representation, usually known as folksonomy. However, for the sake of knowledge workflow, one needs to find a compromise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to address this concern by devising a method that automatically enriches a folksonomy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the ontologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.

@inproceedings{marinho2008folksonomybased, author = {Marinho, Leandro Balby and Buza, Krisztian and Schmidt-Thieme, Lars}, title = {Folksonomy-Based Collabulary Learning.}, editor = {Sheth, Amit P. and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy W. and Thirunarayan, Krishnaprasad}, booktitle = {International Semantic Web Conference}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2008}, volume = {5318}, pages = {261-276}, url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2008.html#MarinhoBS08}, isbn = {978-3-540-88563-4}, keywords = {ol_web2.0, collabulary, enrichment, folksonomy, learning, ontology_learning}, abstract = {The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows down the full materialization of the SemanticWeb since these systems allow ordinary users to create and share knowledge in a simple, cheap, and scalable representation, usually known as folksonomy. However, for the sake of knowledge workflow, one needs to find a compromise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to address this concern by devising a method that automatically enriches a folksonomy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the ontologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.} }

%0 = inproceedings %A = Marinho, Leandro Balby and Buza, Krisztian and Schmidt-Thieme, Lars %B = International Semantic Web Conference %D = 2008 %I = Springer %T = Folksonomy-Based Collabulary Learning. %U = http://dblp.uni-trier.de/db/conf/semweb/iswc2008.html#MarinhoBS08

Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.

@inproceedings{tso2008tag, author = {Tso-Sutter, Karen H. L. and Marinho, Leandro Balby and Schmidt-Thieme, Lars}, title = {Tag-aware recommender systems by fusion of collaborative filtering algorithms}, booktitle = {SAC '08: Proceedings of the 2008 ACM symposium on Applied computing}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {1995--1999}, url = {http://portal.acm.org/citation.cfm?id=1364171}, doi = {http://doi.acm.org/10.1145/1363686.1364171}, isbn = {978-1-59593-753-7}, keywords = {tagging, recommender, tag, collaborative, social, filtering, bookmarking}, abstract = {Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.} }

%0 = inproceedings %A = Tso-Sutter, Karen H. L. and Marinho, Leandro Balby and Schmidt-Thieme, Lars %B = SAC '08: Proceedings of the 2008 ACM symposium on Applied computing %C = New York, NY, USA %D = 2008 %I = ACM %T = Tag-aware recommender systems by fusion of collaborative filtering algorithms %U = http://portal.acm.org/citation.cfm?id=1364171

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 two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

@inproceedings{jaeschke2007tag, author = {Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, isbn = {978-3-540-74975-2}, keywords = {recommendations, folkrank}, 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.
this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.} }

%0 = inproceedings %A = Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases %C = Berlin, Heidelberg %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://dx.doi.org/10.1007/978-3-540-74976-9_52

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 two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

@inproceedings{jaeschke2007tag, author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, isbn = {978-3-540-74975-2}, keywords = {2007, tagging, itegpub, folksonomies, nepomuk, ranking, Folksonomies, Recommendations, l3s, myown, recommendations, FolkRank}, 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.
this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.} }

%0 = inproceedings %A = Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases %C = Berlin, Heidelberg %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://dx.doi.org/10.1007/978-3-540-74976-9_52

@inproceedings{jaschke07recommender, author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf}, isbn = {978-3-540-74975-2}, keywords = {2007, tagging, taggingsurvey, pagerank, recommender, summerschool, social, folksonomy, myown, sosbuch, kdubiq} }

%0 = inproceedings %A = Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.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. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

@inproceedings{jaeschke2007tag, author = {Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, isbn = {978-3-540-74975-2}, keywords = {2007, tagging, wp5, recommender, l3s, folksonomy, myown}, 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 two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.} }

%0 = inproceedings %A = Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases %C = Berlin, Heidelberg %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://dx.doi.org/10.1007/978-3-540-74976-9_52

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 two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

@inproceedings{jaeschke2007tag, author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, isbn = {978-3-540-74975-2}, keywords = {2007, tagging, itegpub, folksonomies, nepomuk, ranking, Folksonomies, Recommendations, l3s, myown, recommendations, FolkRank}, 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.
this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.} }

%0 = inproceedings %A = Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases %C = Berlin, Heidelberg %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://dx.doi.org/10.1007/978-3-540-74976-9_52