TY - CONF AU - Landia, Nikolas AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Jäschke, Robert AU - Doerfel, Stephan AU - Mitzlaff, Folke A2 - T1 - Extending FolkRank with content data T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM CY - New York, NY, USA PY - 2012/ M2 - VL - IS - SP - 1 EP - 8 UR - http://doi.acm.org/10.1145/2365934.2365936 M3 - 10.1145/2365934.2365936 KW - tagging KW - social KW - folksonomy KW - bookmarking KW - myown KW - 2012 KW - folkrank L1 - SN - 978-1-4503-1638-5 N1 - Extending FolkRank with content data N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - J?schke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer CY - Heidelberg PY - 2006/06 M2 - VL - 4011 IS - SP - 411 EP - 426 UR - M3 - KW - information KW - informationretrieval KW - pagerank KW - 2006 KW - OntologyHandbook KW - mimose KW - retrieval KW - myown KW - IR KW - itegpub KW - ranking KW - FCA KW - folksonomy KW - folkrank L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - J�schke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer CY - Heidelberg PY - 2006/06 M2 - VL - 4011 IS - SP - 411 EP - 426 UR - http://.kde.cs.uni-kassel.de/hotho M3 - KW - closely_related KW - ranking KW - diploma_thesis KW - search KW - folkrank KW - bibsonomy L1 - hotho06-information.pdf SN - N1 - N1 - AB - Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. ER - TY - CONF AU - Hotho, Andreas AU - J�schke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer CY - Heidelberg PY - 2006/06 M2 - VL - 4011 IS - SP - 411 EP - 426 UR - M3 - KW - information KW - informationretrieval KW - pagerank KW - 2006 KW - OntologyHandbook KW - mimose KW - retrieval KW - myown KW - IR KW - itegpub KW - ranking KW - FCA KW - folksonomy KW - folkrank L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - T1 - Trend detection in folksonomies T2 - Proceedings of the First international conference on Semantic and Digital Media Technologies PB - Springer-Verlag CY - Berlin, Heidelberg PY - 2006/ M2 - VL - IS - SP - 56 EP - 70 UR - http://dx.doi.org/10.1007/11930334_5 M3 - 10.1007/11930334_5 KW - recommender KW - folkrank L1 - SN - 3-540-49335-2, 978-3-540-49335-8 N1 - Trend detection in folksonomies N1 - AB - As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents.

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. ER -