@article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 } @book{balbymarinho2012recommender, abstract = {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.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, year = 2012 } @incollection{jaeschke2012challenges, abstract = {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.}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @article{burke2011recommendation, abstract = {Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.}, author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert}, interhash = {3089ca25de28ef0bc80bcdebd375a6f9}, intrahash = {41dbb2c9f71440c9aa402f8966117979}, journal = {AI Magazine}, number = 3, pages = {46--56}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {Recommendation in the Social Web}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2373}, volume = 32, year = 2011 } @inproceedings{rezel2010swefe, abstract = {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.}, author = {Rezel, R. and Liang, S.}, booktitle = {2010 International Symposium on Collaborative Technologies and Systems (CTS)}, doi = {10.1109/CTS.2010.5478494}, interhash = {9eb696593932c517873232386f8f61bf}, intrahash = {d5b71572c7fea6504a0c0a3d84a9ecf0}, month = may, pages = {349--356}, publisher = {IEEE}, title = {SWE-FE: Extending folksonomies to the Sensor Web}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494}, year = 2010 } @article{citeulike:8506476, abstract = {{Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}}, address = {Los Alamitos, CA, USA}, author = {Santander, Denis P. and Brusilovsky, Peter}, citeulike-article-id = {8506476}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, doi = {10.1109/WI-IAT.2010.261}, interhash = {dd320da969151c01cf270976c0803274}, intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae}, isbn = {978-0-7695-4191-4}, journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, pages = {136--142}, posted-at = {2011-01-05 00:19:36}, priority = {0}, publisher = {IEEE Computer Society}, title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}}, url = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, volume = 1, year = 2010 } @inproceedings{koerner2010thinking, abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.}, address = {Raleigh, NC, USA}, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, interhash = {5afe6e4ce8357d8ac9698060fb438468}, intrahash = {45f8d8f2a8251a5e988c596a5ebb3f2d}, month = apr, publisher = {ACM}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf}, year = 2010 } @inproceedings{benz2008analyzing, abstract = {The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance. }, address = {Dagstuhl, Germany}, author = {Benz, Dominik and Grobelnik, Marko and Hotho, Andreas and Jäschke, Robert and Mladenic, Dunja and Servedio, Vito D. P. and Sizov, Sergej and Szomszor, Martin}, booktitle = {Social Web Communities}, editor = {Alani, Harith and Staab, Steffen and Stumme, Gerd}, interhash = {d738d9d90c1c466ee0a73ac0cc3dc4c1}, intrahash = {7ab57438aa5a68137e46dab8dadd4b2c}, issn = {1862-4405}, number = 08391, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik}, series = {Dagstuhl Seminar Proceedings}, title = {Analyzing Tag Semantics Across Collaborative Tagging Systems}, url = {http://drops.dagstuhl.de/opus/volltexte/2008/1785}, year = 2008 } @misc{Lambiotte2005, abstract = { We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed. }, author = {Lambiotte, R. and Ausloos, M.}, interhash = {7a9dab1c733e8e1982d5f91979749ce9}, intrahash = {65c6f348a54f872fb3e60b4bd64b485b}, note = {cite arxiv:cs.DS/0512090 }, title = {Collaborative tagging as a tripartite network}, url = {http://arxiv.org/abs/cs/0512090}, year = 2005 } @incollection{citeulike:6386729, abstract = {Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.}, address = {Berlin, Heidelberg}, author = {Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi}, booktitle = {Rough Sets and Knowledge Technology }, chapter = 84, citeulike-article-id = {6386729}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, citeulike-linkout-1 = {http://www.springerlink.com/content/f66k11352q386379}, doi = {10.1007/978-3-642-02962-2\_84}, editor = {Wen, Peng and Li, Yuefeng and Polkowski, Lech and Yao, Yiyu and Tsumoto, Shusaku and Wang, Guoyin}, interhash = {80e8a1d0263296925609dbd5b72b7d48}, intrahash = {bf98d7c1fee5f2f188f529701e70199f}, isbn = {978-3-642-02961-5}, pages = {666--673}, posted-at = {2009-12-15 15:06:20}, priority = {2}, publisher = {Springer Berlin Heidelberg}, title = {Tag Based Collaborative Filtering for Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, volume = 5589, year = 2009 } @inproceedings{Halpin_et_al_2006, author = {Halpin, Harry and Robu, Valentin and Shepard, Hana}, booktitle = {Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)}, interhash = {86b08d03b5f0bd947fd9095dc2c9a70c}, intrahash = {4d96572d49c58d1f958731c5050b5f5b}, publisher = {CEUR-WS}, title = {The Dynamics and Semantics of Collaborative Tagging }, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-209/saaw06-full01-halpin.pdf}, volume = {Vol-209}, year = 2006 } @article{ScottA._Golder04012006, abstract = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. }, author = {Golder, Scott A. and Huberman, Bernardo A.}, doi = {10.1177/0165551506062337}, eprint = {http://jis.sagepub.com/cgi/reprint/32/2/198.pdf}, interhash = {df675e16fcba9cd0f6afc5c9f2a8a723}, intrahash = {f67d3599f5282425b8e0e5b383d436a0}, journal = {Journal of Information Science}, number = 2, pages = {198-208}, title = {{Usage patterns of collaborative tagging systems}}, url = {http://jis.sagepub.com/cgi/content/abstract/32/2/198}, volume = 32, year = 2006 } @misc{cattuto-2007, author = {Cattuto, Ciro and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio}, interhash = {7de017393b2d48335e209a9db23e08b6}, intrahash = {4d6bfbd16ba976f4a4aae985c2a92449}, title = {Vocabulary growth in collaborative tagging systems}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0704.3316}, year = 2007 } @inproceedings{Byde2007, abstract = {This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.}, author = {Byde, Andrew and Wan, Hui and Cayzer, Steve}, booktitle = {Proceedings of the International Conference on Weblogs and Social Media}, interhash = {38aaca7e5b9c508a5901f4109dabaa69}, intrahash = {157846898c1c2a65c265a913ebac115a}, month = {March}, priority = {5}, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, url = {http://www.icwsm.org/papers/paper47.html}, year = 2007 } @misc{goettingen2006, abstract = {Präsentation von Lambert Heller an der SUB Göttingen}, author = {Heller, Lambert}, interhash = {f2eca1f9df3fd4ffb7712bad4d21fd6b}, intrahash = {a3850d994dd807a9a05cd1acd9f99a85}, month = {July}, title = {Social Bookmarking und Folksonomies -- gemeinschaftliche Sacherschließung im Web}, url = {http://wikify.org/SoBoFo.pdf}, year = 2006 } @techreport{citeulike:739394, abstract = {Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.}, author = {Heymann, Paul and Garcia-Molina, Hector}, citeulike-article-id = {739394}, institution = {Computer Science Department}, interhash = {d77846b40aadb0e25233cabf905bb93e}, intrahash = {3b4ce6fd7fa6dbf1c39fd261fa39fcd6}, month = {April}, number = {2006-10}, priority = {3}, school = {Standford University}, title = {Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems}, url = {http://dbpubs.stanford.edu:8090/pub/2006-10}, year = 2006 } @misc{cattuto-2006, abstract = {Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns.}, author = {Cattuto, Ciro and Loreto, Vittorio and Pietronero, Luciano}, interhash = {59b1bd0ed96f41d2c3c98ff232df5dd2}, intrahash = {8d265ea13915a79ec08fe13b8e7074c7}, title = {Collaborative Tagging and Semiotic Dynamics}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0605015}, year = 2006 } @article{Macgregor04, author = {Macgregor, George and McCulloch, Emma}, interhash = {d8e65154bec6b250a452000abedde7bc}, intrahash = {dc0d4a5975ff28952d470193001157bd}, journal = {Library Review}, number = 5, title = {Collaborative Tagging as a Knowledge Organisation and Resource Discovery Tool}, url = {http://eprints.rclis.org/archive/00005703/}, volume = 55, year = {in press} }