One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.}, acmid = {2080225}, address = {Berlin, Heidelberg}, author = {Hotho, Andreas and J\"{a}schke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the First international conference on Semantic and Digital Media Technologies}, doi = {10.1007/11930334_5}, interhash = {33e9a7059f7063a9f5fa40dccf95e7c9}, intrahash = {d13084c0e42631a37611cef045ce6864}, isbn = {3-540-49335-2, 978-3-540-49335-8}, location = {Athens, Greece}, numpages = {15}, pages = {56--70}, publisher = {Springer-Verlag}, series = {SAMT'06}, title = {Trend detection in folksonomies}, url = {http://dx.doi.org/10.1007/11930334_5}, year = 2006 } @inproceedings{kim2011personalized, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.}, acmid = {2043945}, address = {New York, NY, USA}, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2043945}, interhash = {1004b267b14d0abde0f8ac3a7ceadd38}, intrahash = {f022e60c5928e01c701d7ec539ec221b}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {8}, pages = {45--52}, publisher = {ACM}, series = {RecSys '11}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, url = {http://doi.acm.org/10.1145/2043932.2043945}, year = 2011 } @inproceedings{Doerfel:2012:LPM:2365934.2365937, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, acmid = {2365937}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {e5c2266da34a9167352615827cc4670d}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, numpages = {8}, pages = {9--16}, publisher = {ACM}, series = {RSWeb '12}, title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation}, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @inproceedings{Landia:2012:EFC:2365934.2365936, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.
Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.}, acmid = {2365936}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and J\"{a}schke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365936}, interhash = {2ce2874d37fd3b90c9f6a46a7a08e94b}, intrahash = {a97bf903435d6fc4fc61e2bb7e3913b9}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, numpages = {8}, pages = {1--8}, publisher = {ACM}, series = {RSWeb '12}, title = {Extending FolkRank with content data}, url = {http://doi.acm.org/10.1145/2365934.2365936}, year = 2012 } @inproceedings{doerfel2012leveraging, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {64bf590675a833770b7d284871435a8d}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, month = sep, pages = {9--16}, publisher = {ACM}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation }, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @inproceedings{landia2012extending, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365936}, interhash = {7400e35f8d412d15722fe3399aba14a3}, intrahash = {b16dabcd7e17b673c34608ac820ce3c7}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, month = sep, pages = {1--8}, publisher = {ACM}, title = {Extending FolkRank with Content Data}, url = {http://doi.acm.org/10.1145/2365934.2365936}, year = 2012 } @inproceedings{gemmell2009improving, abstract = {Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.}, author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web}, editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred}, interhash = {0900f921d87c5ee19a4ed2c70e5a71df}, intrahash = {6b1ff3b7b691b84288fb7122968134c4}, issn = {1613-0073}, month = oct, pages = {17--24}, series = {CEUR-WS.org}, title = {Improving Folkrank With Item-Based Collaborative Filtering}, url = {http://ceur-ws.org/Vol-532/paper3.pdf}, volume = 532, year = 2009 } @inproceedings{kim2011personalized, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.}, acmid = {2043945}, address = {New York, NY, USA}, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2043945}, interhash = {1004b267b14d0abde0f8ac3a7ceadd38}, intrahash = {f022e60c5928e01c701d7ec539ec221b}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {8}, pages = {45--52}, publisher = {ACM}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, url = {http://doi.acm.org/10.1145/2043932.2043945}, year = 2011 } @inproceedings{hotho2006das, abstract = {Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.}, address = {Baden-Baden}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Social Software in der Wertschöpfung}, file = {hotho2006das.pdf:hotho2006das.pdf:PDF}, groups = {public}, interhash = {1b39e4a77cac919f9030601711aad543}, intrahash = {a333df6fdc7ff9322e3ce03988a7965e}, pdf = {E:\home\help_of_all_helps.pdf}, publisher = {Nomos}, timestamp = {2009-09-29 12:35:44}, title = {Das Entstehen von Semantik in BibSonomy}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006entstehen.pdf}, username = {dbenz}, year = 2006 } @article{jaeschke2008tag, 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. }, address = {Amsterdam}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, doi = {10.3233/AIC-2008-0438}, editor = {Giunchiglia, Enrico}, interhash = {b2f1aba6829affc85d852ea93a8e39f7}, intrahash = {955bcf14f3272ba6eaf3dadbef6c0b10}, issn = {0921-7126}, journal = {AI Communications}, number = 4, pages = {231-247}, publisher = {IOS Press}, title = {Tag Recommendations in Social Bookmarking Systems}, url = {http://dx.doi.org/10.3233/AIC-2008-0438}, vgwort = {63}, volume = 21, year = 2008 } @inproceedings{hotho2006entstehen, abstract = {Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.}, address = {Baden-Baden}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Social Software in der Wertschöpfung}, interhash = {1b39e4a77cac919f9030601711aad543}, intrahash = {a333df6fdc7ff9322e3ce03988a7965e}, publisher = {Nomos}, title = {Das Entstehen von Semantik in BibSonomy}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006entstehen.pdf}, year = 2006 }