@incollection{marinho2011social, 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.}, address = {New York}, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, doi = {10.1007/978-0-387-85820-3_19}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1}, intrahash = {708be7b5c269bd3a9d3d2334f858d52d}, isbn = {978-0-387-85820-3}, pages = {615--644}, publisher = {Springer}, title = {Social Tagging Recommender Systems}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, year = 2011 } @article{milicevic2010social, abstract = {Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.}, author = {Milicevic, Aleksandra and Nanopoulos, Alexandros and Ivanovic, Mirjana}, doi = {10.1007/s10462-009-9153-2}, interhash = {d9e331d46fe4da88400036ad404c4535}, intrahash = {efb6cb6220dfdd1e3d9ca4894e9f1459}, issn = {0269-2821}, journal = {Artificial Intelligence Review}, month = jan, number = 3, pages = {187--209}, publisher = {Springer Netherlands}, title = {Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions}, url = {http://dx.doi.org/10.1007/s10462-009-9153-2}, volume = 33, year = 2010 } @incollection{marinho2011social, 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.}, address = {New York}, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, doi = {10.1007/978-0-387-85820-3_19}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1}, intrahash = {708be7b5c269bd3a9d3d2334f858d52d}, isbn = {978-0-387-85820-3}, pages = {615--644}, publisher = {Springer}, title = {Social Tagging Recommender Systems}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, vgwort = {50}, year = 2011 } @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}, booktitle = {Recommender Systems Handbook}, crossref = {reference/rsh/2011}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, ee = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1}, intrahash = {8a520671b6ced7c4b81b1cd18274e0ee}, isbn = {978-0-387-85819-7}, pages = {615-644}, publisher = {Springer}, title = {Social Tagging Recommender Systems.}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11}, year = 2011 } @inproceedings{rendle2009learning, 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.}, address = {New York, NY, USA}, author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/1557019.1557100}, interhash = {1cc85ca2ec82db2a3caf40fd1795a58a}, intrahash = {1bd672ffb8d6ba5589bb0c7deca09412}, isbn = {978-1-60558-495-9}, location = {Paris, France}, pages = {727--736}, publisher = {ACM}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100}, year = 2009 } @inproceedings{rendle2009learning, 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.}, address = {New York, NY, USA}, author = {Rendle, Steffen and Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/1557019.1557100}, interhash = {1cc85ca2ec82db2a3caf40fd1795a58a}, intrahash = {1bd672ffb8d6ba5589bb0c7deca09412}, isbn = {978-1-60558-495-9}, location = {Paris, France}, pages = {727--736}, publisher = {ACM}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100}, year = 2009 } @inproceedings{1454017, address = {New York, NY, USA}, author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454017}, interhash = {8ee38f4ffc05845fcb98f121fb265d48}, intrahash = {e93afe409833a632af02290bbe134cba}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {43--50}, publisher = {ACM}, title = {Tag recommendations based on tensor dimensionality reduction}, url = {http://portal.acm.org/citation.cfm?id=1454017}, year = 2008 } @inproceedings{1557100, 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.}, address = {New York, NY, USA}, author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {http://doi.acm.org/10.1145/1557019.1557100}, interhash = {1cc85ca2ec82db2a3caf40fd1795a58a}, intrahash = {1bd672ffb8d6ba5589bb0c7deca09412}, isbn = {978-1-60558-495-9}, location = {Paris, France}, pages = {727--736}, publisher = {ACM}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, url = {http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD}, year = 2009 } @inproceedings{1454017, address = {New York, NY, USA}, author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454017}, interhash = {8ee38f4ffc05845fcb98f121fb265d48}, intrahash = {e93afe409833a632af02290bbe134cba}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {43--50}, publisher = {ACM}, title = {Tag recommendations based on tensor dimensionality reduction}, url = {http://portal.acm.org/citation.cfm?id=1454017}, year = 2008 }