@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
}

@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{lorince2014supertagger,
  author = {Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter},
  interhash = {4af29810e9c882dc18f560527c65de2f},
  intrahash = {014abc7dc30e38859c5e8605dce1a8f6},
  title = {“Supertagger” Behavior in Building Folksonomies},
  year = 2014
}

@inproceedings{heckner2009personal,
  address = {San Jose, CA, USA},
  author = {Heckner, Markus and Heilemann, Michael and Wolff, Christian},
  booktitle = {Int'l AAAI Conference on Weblogs and Social Media (ICWSM)},
  interhash = {f954e699dc6ca2d0abbe5f6ebe166dc7},
  intrahash = {d1074484ea350ad88400fe4fc6984874},
  month = may,
  title = {Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems},
  year = 2009
}

@techreport{doerfel2014course,
  abstract = {Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior.},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  interhash = {65f287480af20fc407f7d26677f17b72},
  intrahash = {e360f0bd207806e72305efe16491ebe3},
  note = {cite arxiv:1401.0629},
  title = {Of course we share! Testing Assumptions about Social Tagging Systems},
  url = {http://arxiv.org/abs/1401.0629},
  year = 2014
}

@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
}

@inproceedings{doerfel2013analysis,
  abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.},
  acmid = {2507222},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert},
  booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
  doi = {10.1145/2507157.2507222},
  interhash = {3eaf2beb1cdad39b7c5735a82c3338dd},
  intrahash = {aa4b3d79a362d7415aaa77625b590dfa},
  isbn = {978-1-4503-2409-0},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {343--346},
  publisher = {ACM},
  series = {RecSys '13},
  title = {An analysis of tag-recommender evaluation procedures},
  url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
  year = 2013
}

@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
}

@mastersthesis{bottger2012konzept,
  abstract = {Kollaborative Verschlagwortungssysteme bieten Nutzern die Möglichkeit zur freien Verschlagwortung von Ressourcen im World Wide Web. Sie ermöglichen dem Nutzer beliebige Ressourcen mit frei wählbaren Schlagwörtern – so genannten Tags – zu versehen (Social Tagging). Im weiteren Sinne ist Social Tagging nichts anderes als das Indexieren von Ressourcen durch die Nutzenden selbst. Dabei sind die Tag-Zuordnungen für den einzelnen Nutzer und für die gesamte Community in vielerlei Hinsicht hilfreich. So können durch Tags persönliche Ideen oder Wertungen für eine Ressource ausgedrückt werden.  Außerdem können Tags als Kommunikationsmittel von den Nutzern oder Nutzergruppen untereinander verwendet werden. Tags helfen zudem bei der Navigation, beim Suchen und beim zufälligen Entdecken von neuen Ressourcen. Das Verschlagworten der Ressourcen ist für unbedarfte Anwender eine kognitiv anspruchsvolle Aufgabe. Als Unterstützung können Tag-Recommender eingesetzt werden, die Nutzern passende Tags vorschlagen sollen.

UniVideo ist das Videoportal der Universität Kassel, das jedem Mitglied der Hochschule ermöglicht Videos bereitzustellen und weltweit über das WWW abrufbar zu machen. Die bereitgestellten Videos müssen von ihren Eigentümern beim Hochladen verschlagwortet werden. Die dadurch entstehende Struktur dient wiederum als Grundlage für die Navigation in UniVideo. In dieser Arbeit werden vier verschiedene Ansätze für Tag-Recommender theoretisch diskutiert und deren praktische Umsetzung für UniVideo untersucht und bewertet. Dabei werden zunächst die Grundlagen des Social Taggings erläutert und der Aufbau von UniVideo erklärt, bevor die Umsetzung der vier einzelnen Tag-Recommender beschrieben wird. Anschließend wird gezeigt wie aus den einzelnen Tag-Recommendern durch Verschmelzung ein hybrider Tag-Recommender umgesetzt werden kann.},
  address = {Kassel},
  author = {Böttger, Sebastian},
  interhash = {8fd8ce9278d61f8bd5292d7aeab9aacd},
  intrahash = {3c2ffd52e7081b66bf420f993d9144bb},
  month = {04},
  school = {Universität Kassel},
  title = {Konzept und Umsetzung eines Tag-Recommenders für Video-Ressourcen am Beispiel UniVideo},
  type = {Bachelor Thesis},
  url = {http://www.uni-kassel.de/~seboettg/ba-thesis.pdf},
  year = 2012
}

@inproceedings{angelova2008characterizing,
  abstract = {Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution. },
  author = {Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł},
  booktitle = {Proceedings of the Mining Social Data Workshop (MSoDa)},
  interhash = {f74d27a66d2754f3d5892d68c4abee4c},
  intrahash = {02d6739886a13180dd92fbb7243ab58b},
  month = jul,
  organization = {ECAI 2008},
  pages = {21--25},
  title = {Characterizing a social bookmarking and tagging network},
  url = {http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf},
  year = 2008
}

@article{10.1109/TKDE.2012.115,
  address = {Los Alamitos, CA, USA},
  author = {Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P.},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115},
  interhash = {f2e961e2b99fec0634b0d4fa3e001282},
  intrahash = {8a25332bfeb33e2ad8e1e1a062976da2},
  issn = {1041-4347},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  number = {PrePrints},
  publisher = {IEEE Computer Society},
  title = {Harnessing Folksonomies to Produce a Social Classification of Resources},
  volume = 99,
  year = 2012
}

@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{Laniado2010,
  author = {Laniado, David and Mika, Peter},
  booktitle = {International Semantic Web Conference (1)},
  crossref = {conf/semweb/2010-1},
  editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte},
  ee = {http://dx.doi.org/10.1007/978-3-642-17746-0_30},
  interhash = {3a63f88e11f958d548fa91fe442e1dcf},
  intrahash = {58dace4881efbd12c81ef1cc2e6bf7b9},
  isbn = {978-3-642-17745-3},
  pages = {470-485},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Making Sense of Twitter.},
  url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10},
  volume = 6496,
  year = 2010
}

@inproceedings{Landia:2012:EFC:2365934.2365936,
  abstract = {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.</p> <p>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.},
  acmid = {2365936},
  address = {New York, NY, USA},
  author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and J\"{a}schke, Robert and Doerfel, Stephan and Mitzlaff, Folke},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365936},
  interhash = {2ce2874d37fd3b90c9f6a46a7a08e94b},
  intrahash = {a97bf903435d6fc4fc61e2bb7e3913b9},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  numpages = {8},
  pages = {1--8},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Extending FolkRank with content data},
  url = {http://doi.acm.org/10.1145/2365934.2365936},
  year = 2012
}

@inproceedings{rendle2010pairwise,
  abstract = {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.</p> <p>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.},
  acmid = {1718498},
  address = {New York, NY, USA},
  author = {Rendle, Steffen and Schmidt-Thieme, Lars},
  booktitle = {Proceedings of the third ACM international conference on Web search and data mining},
  doi = {10.1145/1718487.1718498},
  interhash = {ce8fbdf2afb954579cdb58104fb683a7},
  intrahash = {10fe730b391b08031f3103f9cdbb6e1a},
  isbn = {978-1-60558-889-6},
  location = {New York, New York, USA},
  numpages = {10},
  pages = {81--90},
  publisher = {ACM},
  title = {Pairwise interaction tensor factorization for personalized tag recommendation},
  url = {http://doi.acm.org/10.1145/1718487.1718498},
  year = 2010
}

@incollection{gemmell2010resource,
  abstract = {Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.},
  address = {Berlin/Heidelberg},
  affiliation = {Center for Web Intelligence, School of Computing, DePaul University, Chicago, Illinois USA},
  author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin},
  booktitle = {E-Commerce and Web Technologies},
  doi = {10.1007/978-3-642-15208-5_1},
  editor = {Buccafurri, Francesco and Semeraro, Giovanni},
  interhash = {357183305397b19624ec246b915df6ac},
  intrahash = {684579385b3a4f90f5b41ce7c92ddb2a},
  isbn = {978-3-642-15208-5},
  keyword = {Computer Science},
  pages = {1--12},
  publisher = {Springer},
  series = {Lecture Notes in Business Information Processing},
  title = {Resource Recommendation in Collaborative Tagging Applications},
  url = {http://dx.doi.org/10.1007/978-3-642-15208-5_1},
  volume = 61,
  year = 2010
}

@inproceedings{navarrobullock2011tagging,
  abstract = {Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.},
  address = {New York, NY, USA},
  author = {Navarro Bullock, Beate and Jäschke, Robert and Hotho, Andreas},
  booktitle = {Proceedings of the ACM WebSci Conference},
  interhash = {7afaa67dfeb07f7e0b85abf2be61aff1},
  intrahash = {e5a4b67ed6173e9645aab321019efd74},
  location = {Koblenz, Germany},
  month = jun,
  organization = {ACM},
  pages = {1--4},
  title = {Tagging data as implicit feedback for learning-to-rank},
  url = {http://journal.webscience.org/463/},
  vgwort = {14,8},
  year = 2011
}

@inproceedings{parra2009evaluation,
  abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. },
  author = {Parra, Denis and Brusilovsky, Peter},
  booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web},
  interhash = {03a51e24ecab3ad66fcc381980144fea},
  intrahash = {42773258c36ccf2f59749991518d1784},
  issn = {1613-0073},
  location = {Torino, Italy},
  month = jun,
  series = {CEUR Workshop Proceedings},
  title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike},
  url = {http://ceur-ws.org/Vol-467/paper5.pdf},
  volume = 467,
  year = 2009
}

@inproceedings{lee2010using,
  abstract = {This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.},
  acmid = {1864752},
  address = {New York, NY, USA},
  author = {Lee, Danielle H. and Brusilovsky, Peter},
  booktitle = {Proceedings of the fourth ACM conference on Recommender systems},
  doi = {10.1145/1864708.1864752},
  interhash = {6fd1cbcfd94da174c910d9144467372a},
  intrahash = {ec592568ca4a9f6b2ebaf41816af1ebc},
  isbn = {978-1-60558-906-0},
  location = {Barcelona, Spain},
  numpages = {4},
  pages = {221--224},
  publisher = {ACM},
  title = {Using self-defined group activities for improving recommendations in collaborative tagging systems},
  url = {http://doi.acm.org/10.1145/1864708.1864752},
  year = 2010
}