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 - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM CY - PY - 2013/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - iteg KW - itegpub KW - l3s KW - myown KW - recommendation KW - rsweb KW - sensor KW - sitc KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based
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 - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM CY - PY - 2013/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - myown KW - recommendation KW - rsweb KW - sensor KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based
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 - 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 CY - Berlin/Heidelberg PY - 2011/ M2 - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 M3 - 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 - 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 CY - Berlin/Heidelberg PY - 2011/ M2 - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 M3 - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub 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 - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - T1 - A Comparison of content-based Tag Recommendations in Folksonomy Systems T2 - Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007) PB - Springer CY - PY - 2011/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2011 KW - content KW - folksonomy KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub L1 - SN - N1 - N1 - AB - ER - TY - THES AU - Jäschke, Robert T1 - Formal concept analysis and tag recommendations in collaborative tagging systems PY - 2011/ PB - SP - EP - UR - http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325 M3 - KW - bibsonomy KW - bookmarking KW - dissertation KW - fca KW - recommender KW - social KW - tag KW - tagging KW - taggingsurvey L1 - N1 - N1 - AB - ER - TY - CHAP AU - Kubatz, Marius AU - Gedikli, Fatih AU - Jannach, Dietmar A2 - Huemer, Christian A2 - Setzer, Thomas T1 - LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations T2 - E-Commerce and Web Technologies PB - Springer Berlin Heidelberg CY - PY - 2011/ VL - 85 IS - SP - 258 EP - 269 UR - http://dx.doi.org/10.1007/978-3-642-23014-1_22 M3 - 10.1007/978-3-642-23014-1_22 KW - folkrank KW - leavepostout KW - localrank KW - recommender KW - tag L1 - SN - 978-3-642-23013-4 N1 - LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations - Springer N1 - AB - On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular ER - TY - JOUR AU - Montañés, Elena AU - Ramón Quevedo, José AU - Díaz, Irene AU - Cortina, Raquel AU - Alonso, Pedro AU - Ranilla, José T1 - TagRanker: learning to recommend ranked tags JO - Logic Journal of IGPL PY - 2011/ VL - 19 IS - 2 SP - 395 EP - 404 UR - http://jigpal.oxfordjournals.org/content/19/2/395.abstract M3 - 10.1093/jigpal/jzq036 KW - LeavePostOut KW - recommender KW - tag KW - tagranker L1 - SN - N1 - N1 - AB - In a social network, recommenders are highly demanded since they provide user interests in order to construct user profiles. This user profiles might be valuable to be exploited in business management or marketing, for instance. Basically, a tag recommender provides to users a set keywords that describe certain resources. The existing approaches require exploiting content information or they just provide a set of tags without any kind of preference order. This article proposes TagRanker, a tag recommender based on logistic regression that is free of exploiting content information. In addition, it gives a ranking of certain tags and learns just from the relations among users, resources and tags previously posted avoiding the cost of exploiting the content of the resources. An adequate evaluation measure for this specific kind of ranking is also proposed, since the existing ones just consider the tags as coming from a classification. The experiments on several data sets show that TagRanker can effectively recommend relevant tags outperforming the performance of a benchmark of Tag Recommender Systems. ER - TY - JOUR AU - Zhang, Zi-Ke AU - Zhou, Tao AU - Zhang, Yi-Cheng T1 - Tag-Aware Recommender Systems: A State-of-the-Art Survey JO - Journal of Computer Science and Technology PY - 2011/ VL - 26 IS - 5 SP - 767 EP - 777 UR - http://dx.doi.org/10.1007/s11390-011-0176-1 M3 - 10.1007/s11390-011-0176-1 KW - recommender KW - survey KW - tag KW - tagging L1 - SN - N1 - N1 - AB - In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms. ER - TY - CONF AU - Gemmell, Jonathan AU - Schimoler, Thomas AU - Mobasher, Bamshad AU - Burke, Robin A2 - T1 - Hybrid tag recommendation for social annotation systems T2 - Proceedings of the 19th ACM international conference on Information and knowledge management PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 829 EP - 838 UR - http://doi.acm.org/10.1145/1871437.1871543 M3 - 10.1145/1871437.1871543 KW - hybrid KW - recommendation KW - tag L1 - SN - 978-1-4503-0099-5 N1 - Hybrid tag recommendation for social annotation systems N1 - AB - Social annotation systems allow users to annotate resources with personalized tags and to navigate large and complex information spaces without the need to rely on predefined hierarchies. These systems help users organize and share their own resources, as well as discover new ones annotated by other users. Tag recommenders in such systems assist users in finding appropriate tags for resources and help consolidate annotations across all users and resources. But the size and complexity of the data, as well as the inherent noise and inconsistencies in the underlying tag vocabularies, have made the design of effective tag recommenders a challenge. Recent efforts have demonstrated the advantages of integrative models that leverage all three dimensions of a social annotation system: users, resources and tags. Among these approaches are recommendation models based on matrix factorization. But, these models tend to lack scalability and often hide the underlying characteristics, or "information channels" of the data that affect recommendation effectiveness. In this paper we propose a weighted hybrid tag recommender that blends multiple recommendation components drawing separately on complementary dimensions, and evaluate it on six large real-world datasets. In addition, we attempt to quantify the strength of the information channels in these datasets and use these results to explain the performance of the hybrid. We find our approach is not only competitive with the state-of-the-art techniques in terms of accuracy, but also has the added benefits of being scalable to large real world applications, extensible to incorporate a wide range of recommendation techniques, easily updateable, and more scrutable than other leading methods. ER - TY - CONF AU - Musto, Cataldo AU - Narducci, Fedelucio AU - Lops, Pasquale AU - de Gemmis, Marco A2 - Buccafurri, Francesco A2 - Semeraro, Giovanni T1 - Combining Collaborative and Content-Based Techniques for Tag Recommendation. T2 - E-Commerce and Web Technologies PB - Springer CY - Berlin/Heidelberg PY - 2010/ M2 - VL - 61 IS - SP - 13 EP - 23 UR - http://dx.doi.org/10.1007/978-3-642-15208-5_2 M3 - 10.1007/978-3-642-15208-5_2 KW - collaborative KW - content KW - recommender KW - tag KW - tagging L1 - SN - 978-3-642-15207-8 N1 - N1 - AB - The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender. ER - TY - CONF AU - Rendle, Steffen AU - Schmidt-Thieme, Lars A2 - T1 - Pairwise interaction tensor factorization for personalized tag recommendation T2 - Proceedings of the third ACM international conference on Web search and data mining PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 81 EP - 90 UR - http://doi.acm.org/10.1145/1718487.1718498 M3 - 10.1145/1718487.1718498 KW - factorization KW - pitf KW - recommender KW - tag KW - tensor L1 - SN - 978-1-60558-889-6 N1 - Pairwise interaction tensor factorization for personalized tag recommendation N1 - AB - Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.
In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. ER - TY - CONF AU - Rendle, Steffen AU - Schmidt-Thieme, Lars A2 - T1 - Pairwise interaction tensor factorization for personalized tag recommendation T2 - Proceedings of the third ACM international conference on Web search and data mining PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 81 EP - 90 UR - http://doi.acm.org/10.1145/1718487.1718498 M3 - 10.1145/1718487.1718498 KW - collaborative KW - factorization KW - folksonomy KW - personalization KW - recommender KW - tag KW - tagging KW - tensor L1 - SN - 978-1-60558-889-6 N1 - N1 - AB - Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.
In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. ER - TY - CONF AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - Sheth, Amit P. A2 - Staab, Steffen A2 - Dean, Mike A2 - Paolucci, Massimo A2 - Maynard, Diana A2 - Finin, Timothy W. A2 - Thirunarayan, Krishnaprasad T1 - Semantic Grounding of Tag Relatedness in Social Bookmarking Systems T2 - The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008 PB - Springer CY - Heidelberg PY - 2008/ M2 - VL - 5318 IS - SP - 615 EP - 631 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf M3 - http://dx.doi.org/10.1007/978-3-540-88564-1_39 KW - 2008 KW - grounding KW - iswc2008 KW - itegpub KW - methods_concepthierarchy KW - methods_concepts KW - myown KW - ol_web2.0 KW - relatedness KW - semantic KW - semantic_relatedness KW - similarity KW - sw KW - tag KW - tagging KW - tagorapub L1 - SN - N1 - N1 - AB - Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike 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 - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag Recommendations in Social Bookmarking Systems JO - AI Communications PY - 2008/ VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 M3 - 10.3233/AIC-2008-0438 KW - 2.0 KW - 2008 KW - Recommendations KW - bookmarking KW - itegpub KW - logsonomies KW - myown KW - recommendations KW - recommender KW - social KW - systems KW - tag KW - tagorapub KW - tags KW - web KW - web2.0 KW - web20 L1 - SN - 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 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.
ER - TY - JOUR AU - Sinclair, James AU - Cardew-Hall, Michael T1 - The folksonomy tag cloud: when is it useful? JO - Journal of Information Science PY - 2008/ VL - 34 IS - 1 SP - 15 EP - 29 UR - http://jis.sagepub.com/content/34/1/15.abstract M3 - 10.1177/0165551506078083 KW - cloud KW - tag KW - useful KW - weblog L1 - SN - N1 - The folksonomy tag cloud: when is it useful? N1 - AB -
The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.
ER -