applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1 DO - KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - myown KW - recommender KW - social KW - tagging L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. 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 social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area. ER - 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 C1 - New York, NY, USA PY - 2012/ CY - VL - IS - SP - 1 EP - 8 UR - http://doi.acm.org/10.1145/2365934.2365936 DO - 10.1145/2365934.2365936 KW - 2012 KW - bookmarking KW - folkrank KW - folksonomy KW - myown KW - social KW - tagging 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 - JOUR AU - Zhang, Yin AU - Zhang, Bin AU - Gao, Kening AU - Guo, Pengwei AU - Sun, Daming T1 - Combining content and relation analysis for recommendation in social tagging systems JO - Physica A: Statistical Mechanics and its Applications PY - 2012/ VL - 391 IS - 22 SP - 5759 EP - 5768 UR - http://www.sciencedirect.com/science/article/pii/S0378437112003846 DO - 10.1016/j.physa.2012.05.013 KW - folkrank KW - folksonomy KW - lda KW - model KW - ranking KW - toread L1 - SN - N1 - ScienceDirect.com - Physica A: Statistical Mechanics and its Applications - Combining content and relation analysis for recommendation in social tagging systems N1 - AB - Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks. ER - TY - JOUR AU - Zubiaga, Arkaitz AU - Fresno, Victor AU - Martinez, Raquel AU - Garcia-Plaza, Alberto P. T1 - Harnessing Folksonomies to Produce a Social Classification of Resources JO - IEEE Transactions on Knowledge and Data Engineering PY - 2012/ VL - 99 IS - PrePrints SP - EP - UR - DO - http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115 KW - classification KW - delicious KW - folksonomy KW - tagging KW - toread KW - dataset L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - myown KW - recommendations KW - recommender KW - tag L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. 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 C1 - PY - 2011/ VL - IS - SP - 615 EP - 644 UR - http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11 DO - KW - 2011 KW - folksonomy KW - myown KW - recommender KW - tagging KW - taggingsurvey L1 - SN - 978-0-387-85819-7 N1 - N1 - AB - ER - TY - CONF AU - Laniado, David AU - Mika, Peter A2 - Patel-Schneider, Peter F. A2 - Pan, Yue A2 - Hitzler, Pascal A2 - Mika, Peter A2 - Zhang, Lei A2 - Pan, Jeff Z. A2 - Horrocks, Ian A2 - Glimm, Birte T1 - Making Sense of Twitter. T2 - International Semantic Web Conference (1) PB - Springer C1 - PY - 2010/ CY - VL - 6496 IS - SP - 470 EP - 485 UR - http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10 DO - KW - analysis KW - folksonomy KW - tagging KW - toread KW - twitter L1 - SN - 978-3-642-17745-3 N1 - N1 - AB - ER - TY - CONF AU - Rezel, R. AU - Liang, S. A2 - T1 - SWE-FE: Extending folksonomies to the Sensor Web T2 - 2010 International Symposium on Collaborative Technologies and Systems (CTS) PB - IEEE C1 - PY - 2010/05 CY - VL - IS - SP - 349 EP - 356 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494 DO - 10.1109/CTS.2010.5478494 KW - collaborative KW - everyaware KW - folksonomy KW - sensor KW - tagging KW - taggingsurvey KW - toread L1 - SN - N1 - N1 - AB - This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment. ER - TY - CONF AU - Wetzker, Robert AU - Zimmermann, Carsten AU - Bauckhage, Christian AU - Albayrak, Sahin A2 - T1 - I tag, you tag: translating tags for advanced user models T2 - Proceedings of the third ACM international conference on Web search and data mining PB - ACM C1 - New York, NY, USA PY - 2010/ CY - VL - IS - SP - 71 EP - 80 UR - http://doi.acm.org/10.1145/1718487.1718497 DO - 10.1145/1718487.1718497 KW - folksonomy KW - model KW - recommender KW - tagging KW - taggingsurvey L1 - SN - 978-1-60558-889-6 N1 - I tag, you tag N1 - AB - Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios. ER - TY - THES AU - Bogers, Toine T1 - Recommender Systems for Social Bookmarking PY - 2009/12 PB - Tilburg University SP - EP - UR - http://ilk.uvt.nl/~toine/phd-thesis/ DO - KW - bookmarking KW - dissertation KW - folksonomy KW - recommender KW - social KW - tagging KW - taggingsurvey L1 - N1 - N1 - AB - Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. ER - TY - JOUR AU - Eda, Takeharu AU - Yoshikawa, Masatoshi AU - Uchiyama, Toshio AU - Uchiyama, Tadasu T1 - The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags. JO - World Wide Web PY - 2009/ VL - 12 IS - 4 SP - 421 EP - 440 UR - http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09 DO - KW - analysis KW - folksonomy KW - ol KW - semantic KW - taxonomy KW - toread L1 - SN - N1 - dblp N1 - AB - ER - TY - CONF AU - Angeletou, Sofia AU - Sabou, Marta AU - Motta, Enrico A2 - T1 - Semantically enriching folksonomies with FLOR T2 - In Proc of the 5th ESWC. workshop: Collective Intelligence & the Semantic Web PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2569 DO - KW - folksonomy KW - ontologies KW - semantic KW - tagging KW - taggingsurvey L1 - SN - N1 - Semantically enriching folksonomies with FLOR N1 - AB - Abstract. While the increasing popularity of folksonomies has lead to a vast quantity of tagged data, resource retrieval in folksonomies is limited by being agnostic to the meaning (i.e., semantics) of tags. Our goal is to automatically enrich folksonomy tags (and implicitly the related resources) with formal semantics by associating them to relevant concepts defined in online ontologies. We introduce FLOR, a method that performs automatic folksonomy enrichment by combining knowledge from WordNet and online available ontologies. Experimentally testing FLOR, we found that it correctly enriched 72 % of 250 Flickr photos. 1 ER - TY - CONF AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Grounding of Tag Relatedness in Social Bookmarking Systems T2 - The Semantic Web - ISWC 2008 PB - Springer Berlin / Heidelberg C1 - PY - 2008/ CY - VL - 5318 IS - SP - 615 EP - 631 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf DO - 10.1007/978-3-540-88564-1_39 KW - 2008 KW - folksonomy KW - grounding KW - iswc2008 KW - myown KW - semantic KW - sw KW - tag KW - tagging KW - taggingsurvey KW - webzu L1 - SN - 978-3-540-88563-4 N1 - SpringerLink - Buchkapitel N1 - AB - Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application. ER - TY - CONF AU - Kim, Hak Lae AU - Scerri, Simon AU - Breslin, John G. AU - Decker, Stefan AU - Kim, Hong Gee A2 - T1 - The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies T2 - Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications PB - Dublin Core Metadata Initiative C1 - Berlin, Deutschland PY - 2008/ CY - VL - IS - SP - 128 EP - 137 UR - DO - KW - folksonomy KW - ontology KW - semantic KW - tag KW - tagging KW - taggingsurvey KW - toread L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - social information retrieval with logdata T2 - HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia PB - ACM C1 - New York, NY, USA PY - 2008/ CY - VL - IS - SP - 157 EP - 166 UR - http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Proceedings&title=HT&CFID=825963&CFTOKEN=78379687 DO - http://doi.acm.org/10.1145/1379092.1379123 KW - 2.0 KW - 2008 KW - folksonomy KW - implicit KW - logsonomy KW - myown KW - web L1 - SN - 978-1-59593-985-2 N1 - HT: HT '08, Logsonomy - social information ... N1 - AB - Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration.
Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance.
This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics. ER - TY - JOUR AU - Cimiano, Philipp AU - Hotho, Andreas AU - Staab, Steffen T1 - Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis JO - Journal on Artificial Intelligence Research PY - 2005/ VL - 24 IS - SP - 305 EP - 339 UR - http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05 DO - KW - 2005 KW - fca KW - folksonomy KW - hierarchies KW - hierarchy KW - learning KW - myown KW - ontologies KW - text L1 - SN - N1 - N1 - AB - ER -