TY - CHAP AU - Balby Marinho, Leandro AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd AU - Symeonidis, Panagiotis A2 - Ricci, Francesco A2 - Rokach, Lior A2 - Shapira, Bracha A2 - Kantor, Paul B. T1 - Social Tagging Recommender Systems T2 - Recommender Systems Handbook PB - Springer CY - New York PY - 2011/ VL - IS - SP - 615 EP - 644 UR - http://dx.doi.org/10.1007/978-0-387-85820-3_19 M3 - 10.1007/978-0-387-85820-3_19 KW - tagging KW - itegpub KW - recommender KW - collaborative KW - 2011 KW - social KW - myown KW - info20 L1 - SN - 978-0-387-85820-3 N1 - N1 - AB - 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. ER - TY - CHAP AU - Balby Marinho, Leandro AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd AU - Symeonidis, Panagiotis A2 - Ricci, Francesco A2 - Rokach, Lior A2 - Shapira, Bracha A2 - Kantor, Paul B. T1 - Social Tagging Recommender Systems T2 - Recommender Systems Handbook PB - Springer CY - New York PY - 2011/ VL - IS - SP - 615 EP - 644 UR - http://dx.doi.org/10.1007/978-0-387-85820-3_19 M3 - 10.1007/978-0-387-85820-3_19 KW - tagging KW - recommender KW - collaborative KW - 2011 KW - social KW - myown L1 - SN - 978-0-387-85820-3 N1 - N1 - AB - 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. ER - TY - CHAP AU - Marinho, Leandro Balby AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd AU - Symeonidis, Panagiotis A2 - Ricci, Francesco A2 - Rokach, Lior A2 - Shapira, Bracha A2 - Kantor, Paul B. T1 - Social Tagging Recommender Systems. T2 - Recommender Systems Handbook PB - Springer CY - PY - 2011/ VL - IS - SP - 615 EP - 644 UR - http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11 M3 - KW - tagging KW - taggingsurvey KW - recommender KW - 2011 KW - folksonomy KW - myown L1 - SN - 978-0-387-85819-7 N1 - N1 - AB - ER - TY - CONF AU - Rendle, Steffen AU - Marinho, Leandro Balby AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars A2 - T1 - Learning optimal ranking with tensor factorization for tag recommendation T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 727 EP - 736 UR - http://portal.acm.org/citation.cfm?doid=1557019.1557100 M3 - 10.1145/1557019.1557100 KW - factorization KW - ranking KW - folksonomy KW - learning KW - optimal KW - folkrank KW - tensor L1 - SN - 978-1-60558-495-9 N1 - Learning optimal ranking with tensor factorization for tag recommendation N1 - AB - 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. ER - TY - CONF AU - Rendle, Steffen AU - Balby Marinho, Leandro AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars A2 - T1 - Learning optimal ranking with tensor factorization for tag recommendation T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 727 EP - 736 UR - http://portal.acm.org/citation.cfm?doid=1557019.1557100 M3 - 10.1145/1557019.1557100 KW - tagging KW - recommender KW - tag KW - ranking KW - folksonomy KW - tensor L1 - SN - 978-1-60558-495-9 N1 - N1 - AB - 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. ER - TY - CONF AU - Rendle, Steffen AU - Marinho, Leandro Balby AU - Nanopoulos, Alexandros AU - Schmidt-Thieme, Lars A2 - T1 - Learning optimal ranking with tensor factorization for tag recommendation T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 727 EP - 736 UR - http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD M3 - http://doi.acm.org/10.1145/1557019.1557100 KW - tagging KW - taggingsurvey KW - recommender KW - fast KW - folksonomy KW - tensor L1 - SN - 978-1-60558-495-9 N1 - KDD: KDD '09, Learning optimal ranking with ... N1 - AB - 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. ER - TY - CONF AU - Marinho, Leandro Balby AU - Buza, Krisztian AU - Schmidt-Thieme, Lars A2 - Sheth, Amit P. A2 - Staab, Steffen A2 - Dean, Mike A2 - Paolucci, Massimo A2 - Maynard, Diana A2 - Finin, Timothy W. A2 - Thirunarayan, Krishnaprasad T1 - Folksonomy-Based Collabulary Learning. T2 - International Semantic Web Conference PB - Springer CY - PY - 2008/ M2 - VL - 5318 IS - SP - 261 EP - 276 UR - http://dblp.uni-trier.de/db/conf/semweb/iswc2008.html#MarinhoBS08 M3 - KW - ol_web2.0 KW - collabulary KW - enrichment KW - folksonomy KW - learning KW - ontology_learning L1 - SN - 978-3-540-88563-4 N1 - dblp N1 - AB - 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. ER - TY - CONF AU - Tso-Sutter, Karen H. L. AU - Marinho, Leandro Balby AU - Schmidt-Thieme, Lars A2 - T1 - Tag-aware recommender systems by fusion of collaborative filtering algorithms T2 - SAC '08: Proceedings of the 2008 ACM symposium on Applied computing PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 1995 EP - 1999 UR - http://portal.acm.org/citation.cfm?id=1364171 M3 - http://doi.acm.org/10.1145/1363686.1364171 KW - tagging KW - recommender KW - tag KW - collaborative KW - social KW - filtering KW - bookmarking L1 - SN - 978-1-59593-753-7 N1 - Tag-aware recommender systems by fusion of collaborative filtering algorithms N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Balby Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer CY - Berlin, Heidelberg PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 M3 - KW - recommendations KW - folkrank L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Marinho, Leandro Balby AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer CY - Berlin, Heidelberg PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 M3 - KW - 2007 KW - tagging KW - itegpub KW - folksonomies KW - nepomuk KW - ranking KW - Folksonomies KW - Recommendations KW - l3s KW - myown KW - recommendations KW - FolkRank L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Marinho, Leandro Balby AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - 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 PB - Springer CY - PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf M3 - KW - 2007 KW - tagging KW - taggingsurvey KW - pagerank KW - recommender KW - summerschool KW - social KW - folksonomy KW - myown KW - sosbuch KW - kdubiq L1 - SN - 978-3-540-74975-2 N1 - DBLP Record 'conf/pkdd/JaschkeMHSS07' N1 - AB - ER - TY - CONF AU - Jäschke, Robert AU - Balby Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer CY - Berlin, Heidelberg PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 M3 - KW - 2007 KW - tagging KW - wp5 KW - recommender KW - l3s KW - folksonomy KW - myown L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Marinho, Leandro Balby AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer CY - Berlin, Heidelberg PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 M3 - KW - 2007 KW - tagging KW - itegpub KW - folksonomies KW - nepomuk KW - ranking KW - Folksonomies KW - Recommendations KW - l3s KW - myown KW - recommendations KW - FolkRank L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - 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. ER -