@article{glushko2008categorization, abstract = {In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important. Furthermore, key aspects of categorization that have received little previous attention emerge from considering diverse types of categorization together, such as the social factors that create stability in classification systems, and the interoperability that shared conceptual systems establish between agents. Finally, the profound impact of recent technological developments on classification systems indicates that basic categorization mechanisms are highly adaptive, producing new classification systems as the situations in which they operate change. }, author = {Glushko, Robert J. and Maglio, Paul P. and Matlock, Teenie and Barsalou, Lawrence W.}, doi = {http://dx.doi.org/10.1016/j.tics.2008.01.007}, interhash = {f21366054d9e9524e0f90a17e59aca97}, intrahash = {09d23ef22bc50a6c21735e5332f1ecdb}, issn = {1364-6613}, journal = {Trends in Cognitive Sciences }, number = 4, pages = {129 - 135}, title = {Categorization in the wild }, url = {http://www.sciencedirect.com/science/article/pii/S1364661308000557}, volume = 12, year = 2008 } @inproceedings{ames2007motivations, abstract = {Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture. A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.}, acmid = {1240772}, address = {New York, NY, USA}, author = {Ames, Morgan and Naaman, Mor}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, doi = {10.1145/1240624.1240772}, interhash = {bd24c17d66d2b904b3fc9444c2b64b44}, intrahash = {c3840b12cf9592a782a09ab9e1bdf49e}, isbn = {978-1-59593-593-9}, location = {San Jose, California, USA}, numpages = {10}, pages = {971--980}, publisher = {ACM}, series = {CHI '07}, title = {Why We Tag: Motivations for Annotation in Mobile and Online Media}, url = {http://doi.acm.org/10.1145/1240624.1240772}, year = 2007 } @incollection{lorince2015analysis, abstract = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do }, author = {Lorince, Jared and Joseph, Kenneth and Todd, PeterM.}, booktitle = {Social Computing, Behavioral-Cultural Modeling, and Prediction}, doi = {10.1007/978-3-319-16268-3_15}, editor = {Agarwal, Nitin and Xu, Kevin and Osgood, Nathaniel}, interhash = {b6f817ca50d1c44886c9ed58facbf592}, intrahash = {1485f6521c6ae2db520d1a7c3c429f07}, isbn = {978-3-319-16267-6}, language = {English}, pages = {141-152}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, title = {Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?}, url = {http://dx.doi.org/10.1007/978-3-319-16268-3_15}, volume = 9021, year = 2015 } @inproceedings{kaur2014scholarometer, abstract = {Scholarometer (scholarometer.indiana.edu) is a social tool developed to facilitate citation analysis and help evaluate the impact of authors. The Scholarometer service allows scholars to compute various citation-based impact measures. In exchange, users provide disciplinary annotations of authors, which allow for the computation of discipline-specific statistics and discipline-neutral impact metrics. We present here two improvements of our system. First, we integrated a new universal impact metric hs that uses crowdsourced data to calculate the global rank of a scholar across disciplinary boundaries. Second, improvements made in ambiguous name classification have increased the accuracy from 80% to 87%.}, acmid = {2615669}, address = {New York, NY, USA}, author = {Kaur, Jasleen and JafariAsbagh, Mohsen and Radicchi, Filippo and Menczer, Filippo}, booktitle = {Proceedings of the 2014 ACM Conference on Web Science}, doi = {10.1145/2615569.2615669}, interhash = {bfb4274f2a002cde9efbe71faf295e6a}, intrahash = {4edc2b8ed7acdd1ef8be4d6eefea8718}, isbn = {978-1-4503-2622-3}, location = {Bloomington, Indiana, USA}, numpages = {2}, pages = {285--286}, publisher = {ACM}, series = {WebSci '14}, title = {Scholarometer: A System for Crowdsourcing Scholarly Impact Metrics}, url = {http://doi.acm.org/10.1145/2615569.2615669}, year = 2014 } @article{li2012validating, abstract = {This paper investigates whether CiteULike and Mendeley are useful for measuring scholarly influence, using a sample of 1,613 papers published in Nature and Science in 2007. Traditional citation counts from the Web of Science (WoS) were used as benchmarks to compare with the number of users who bookmarked the articles in one of the two free online reference manager sites. Statistically significant correlations were found between the user counts and the corresponding WoS citation counts, suggesting that this type of influence is related in some way to traditional citation-based scholarly impact but the number of users of these systems seems to be still too small for them to challenge traditional citation indexes.}, author = {Li, Xuemei and Thelwall, Mike and Giustini, Dean}, doi = {10.1007/s11192-011-0580-x}, interhash = {9f186a30dbe5af5dec8a49604bcca3dd}, intrahash = {60c73c95336adf02c315c7b4c434cfd4}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {461-471}, publisher = {Springer Netherlands}, title = {Validating online reference managers for scholarly impact measurement}, url = {http://dx.doi.org/10.1007/s11192-011-0580-x}, volume = 91, year = 2012 } @inproceedings{saeed2008citation, abstract = {New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.}, author = {Saeed, A.U. and Afzal, M.T. and Latif, A. and Tochtermann, K.}, booktitle = {Multitopic Conference, 2008. INMIC 2008. IEEE International}, doi = {10.1109/INMIC.2008.4777769}, interhash = {26d1785cab132d577e377bb5bf299002}, intrahash = {677fc89fef6c79a6a4f25cb25246e38a}, month = dec, pages = {392-397}, title = {Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4777769}, year = 2008 } @article{haustein2011applying, abstract = {Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in \{STM\} and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content. }, author = {Haustein, Stefanie and Siebenlist, Tobias}, doi = {http://dx.doi.org/10.1016/j.joi.2011.04.002}, interhash = {13fe59aae3d6ef95b529ffe00ede4126}, intrahash = {c3e49ee7b0ed81ecd126d3ef76d5f407}, issn = {1751-1577}, journal = {Journal of Informetrics }, number = 3, pages = {446 - 457}, title = {Applying social bookmarking data to evaluate journal usage }, url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393}, volume = 5, year = 2011 } @inproceedings{jaeschke2009testing, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.}, address = {New York, NY, USA}, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, booktitle = {RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems}, interhash = {440fafda1eccf4036066f457eb6674a0}, intrahash = {1320904b208d53bd5d49e751cbfcc268}, location = {New York, NY, USA}, note = {(to appear)}, publisher = {ACM}, title = {Testing and Evaluating Tag Recommenders in a Live System}, year = 2009 } @phdthesis{jaschke2011formal, address = {Heidelberg}, author = {Jäschke, Robert}, interhash = {dcb2cd1cd72ae45d77c4d8755d199405}, intrahash = {bad02a0bbbf066907ecdee0ecaf9fb80}, isbn = {1-60750-707-2}, publisher = {Akad. Verl.-Ges. AKA}, series = {Dissertations in artificial intelligence}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038}, volume = 332, year = 2011 } @inproceedings{lipczak2010impact, abstract = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -- folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor -- resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.}, acmid = {1810648}, address = {New York, NY, USA}, author = {Lipczak, Marek and Milios, Evangelos}, booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810648}, interhash = {a999b5f2eace0cd75028e57261afe3d7}, intrahash = {71dd1a473eaf0af9840758653746c221}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, numpages = {10}, pages = {179--188}, publisher = {ACM}, series = {HT '10}, title = {The Impact of Resource Title on Tags in Collaborative Tagging Systems}, url = {http://doi.acm.org/10.1145/1810617.1810648}, year = 2010 } @inproceedings{melville2002contentboosted, abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.}, acmid = {777124}, address = {Menlo Park, CA, USA}, author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass}, booktitle = {Eighteenth National Conference on Artificial Intelligence}, interhash = {985028099c1a29f116ad7434005895ac}, intrahash = {a4917f0299f48e403966a8003ebd50be}, isbn = {0-262-51129-0}, location = {Edmonton, Alberta, Canada}, numpages = {6}, pages = {187--192}, publisher = {American Association for Artificial Intelligence}, title = {Content-boosted Collaborative Filtering for Improved Recommendations}, url = {http://dl.acm.org/citation.cfm?id=777092.777124}, year = 2002 } @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.}, doi = {10.1007/978-1-4614-1894-8}, 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://link.springer.com/book/10.1007/978-1-4614-1894-8}, year = 2012 } @inproceedings{lipczak2010impact, abstract = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -- folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor -- resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.}, acmid = {1810648}, address = {New York, NY, USA}, author = {Lipczak, Marek and Milios, Evangelos}, booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810648}, interhash = {a999b5f2eace0cd75028e57261afe3d7}, intrahash = {71dd1a473eaf0af9840758653746c221}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, numpages = {10}, pages = {179--188}, publisher = {ACM}, series = {HT '10}, title = {The Impact of Resource Title on Tags in Collaborative Tagging Systems}, url = {http://doi.acm.org/10.1145/1810617.1810648}, year = 2010 } @phdthesis{jaschke2011formal, address = {Heidelberg}, author = {Jäschke, Robert}, interhash = {dcb2cd1cd72ae45d77c4d8755d199405}, intrahash = {bad02a0bbbf066907ecdee0ecaf9fb80}, isbn = {1-60750-707-2}, publisher = {Akad. Verl.-Ges. AKA}, series = {Dissertations in artificial intelligence}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038}, volume = 332, year = 2011 } @proceedings{jannach2014proceedings, bibsource = {dblp computer science bibliography, http://dblp.org}, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, interhash = {a1a704ec9c98e6031a1444c6eccc7c0a}, intrahash = {09cb7c63e60bd3c5e6773c9c871a8aba}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, url = {http://ceur-ws.org/Vol-1271}, volume = 1271, year = 2014 } @inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014}, doi = {10.1145/2645710.2645786}, interhash = {b465a3695da123d6ee9de1675cb3d480}, intrahash = {5773f799bec72240eda5e6cfb6a03d7b}, pages = 395, title = {The sixth {ACM} RecSys workshop on recommender systems and the social web}, url = {http://doi.acm.org/10.1145/2645710.2645786}, year = 2014 } @inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, interhash = {9223d6d728612c8c05a80b5edceeb78b}, intrahash = {11fab5468dd4b4e3db662ea5e68df8e0}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 } @inproceedings{doerfel2014evaluating, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Proceedings of the 16th {LWA} Workshops: KDML, {IR} and FGWM, Aachen, Germany, September 8-10, 2014.}, editor = {Seidl, Thomas and Hassani, Marwan and Beecks, Christian}, interhash = {955cd7c6f7652b7c531b699464925b1f}, intrahash = {4b2e73c82b5a84e1959ad66aaad4a235}, pages = {18--19}, publisher = {CEUR-WS.org}, title = {Evaluating Assumptions about Social Tagging - {A} Study of User Behavior in BibSonomy}, url = {http://ceur-ws.org/Vol-1226/paper06.pdf}, year = 2014 } @inproceedings{kowald2014probability, abstract = {In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how incorporating a time-dependent component outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We demonstrate how effective principles of information retrieval can be designed and implemented if human memory processes are taken into account.}, acmid = {2576934}, address = {Republic and Canton of Geneva, Switzerland}, author = {Kowald, Dominik and Seitlinger, Paul and Trattner, Christoph and Ley, Tobias}, booktitle = {Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion}, doi = {10.1145/2567948.2576934}, interhash = {911a425fa4f6e883c32fa7a09840bdd8}, intrahash = {659fca43cb6751ee9b13297b797d91e1}, isbn = {978-1-4503-2745-9}, location = {Seoul, Korea}, numpages = {6}, pages = {463--468}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW Companion '14}, title = {Long Time No See: The Probability of Reusing Tags As a Function of Frequency and Recency}, url = {http://dx.doi.org/10.1145/2567948.2576934}, year = 2014 } @incollection{mitzlaff2011community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, doi = {10.1007/978-3-642-23599-3_5}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {a1c0fd5a9f8c5ddb33b3196927409f36}, isbn = {978-3-642-23598-6}, language = {English}, pages = {79-98}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Community Assessment Using Evidence Networks}, url = {http://dx.doi.org/10.1007/978-3-642-23599-3_5}, volume = 6904, year = 2011 }