One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.}, acmid = {2080225}, address = {Berlin, Heidelberg}, author = {Hotho, Andreas and J\"{a}schke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the First international conference on Semantic and Digital Media Technologies}, doi = {10.1007/11930334_5}, interhash = {33e9a7059f7063a9f5fa40dccf95e7c9}, intrahash = {d13084c0e42631a37611cef045ce6864}, isbn = {3-540-49335-2, 978-3-540-49335-8}, location = {Athens, Greece}, numpages = {15}, pages = {56--70}, publisher = {Springer-Verlag}, series = {SAMT'06}, title = {Trend detection in folksonomies}, url = {http://dx.doi.org/10.1007/11930334_5}, year = 2006 } @inproceedings{kim2011personalized, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.}, acmid = {2043945}, address = {New York, NY, USA}, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2043945}, interhash = {1004b267b14d0abde0f8ac3a7ceadd38}, intrahash = {f022e60c5928e01c701d7ec539ec221b}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {8}, pages = {45--52}, publisher = {ACM}, series = {RecSys '11}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, url = {http://doi.acm.org/10.1145/2043932.2043945}, year = 2011 } @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.
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}, series = {WSDM '10}, title = {Pairwise interaction tensor factorization for personalized tag recommendation}, url = {http://doi.acm.org/10.1145/1718487.1718498}, year = 2010 } @article{gemmell2012resource, abstract = {Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.}, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, doi = {10.1016/j.jcss.2011.10.006}, interhash = {e7a4b630500c6a468c40d0e63ee31455}, intrahash = {de0e3910bd4932b63e5ba6058e5cee45}, issn = {0022-0000}, journal = {Journal of Computer and System Sciences}, number = 4, pages = {1160 - 1174}, title = {Resource recommendation in social annotation systems: A linear-weighted hybrid approach}, url = {http://www.sciencedirect.com/science/article/pii/S0022000011001127}, volume = 78, year = 2012 } @electronic{priem2010scientometrics, abstract = {The growing flood of scholarly literature is exposing the weaknesses of current, citation-based methods of evaluating and filtering articles. A novel and promising approach is to examine the use and citation of articles in a new forum: Web 2.0 services like social bookmarking and microblogging. Metrics based on this data could build a “Scientometics 2.0,” supporting richer and more timely pictures of articles' impact. This paper develops the most comprehensive list of these services to date, assessing the potential value and availability of data from each. We also suggest the next steps toward building and validating metrics drawn from the social Web.}, author = {Priem, Jason and Hemminger, Bradely H.}, interhash = {d38dfec4da93265575aff99a811839d9}, intrahash = {b95d32eed9419fefc007245914faad98}, journal = {First Monday; Volume 15, Number 7 - 5 July 2010}, title = {Scientometrics 2.0: New metrics of scholarly impact on the social Web}, url = {http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2874/2570}, year = 2010 } @inproceedings{McNee:2006:AEA:1125451.1125659, abstract = {Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems.}, acmid = {1125659}, address = {New York, NY, USA}, author = {McNee, Sean M. and Riedl, John and Konstan, Joseph A.}, booktitle = {CHI '06 extended abstracts on Human factors in computing systems}, doi = {10.1145/1125451.1125659}, interhash = {fe396fbce5daacd374196ad688e3f149}, intrahash = {4b9fddbd766a9247856641989a778b23}, isbn = {1-59593-298-4}, location = {Montr\&\#233;al, Qu\&\#233;bec, Canada}, numpages = {5}, pages = {1097--1101}, publisher = {ACM}, series = {CHI EA '06}, title = {Being accurate is not enough: how accuracy metrics have hurt recommender systems}, url = {http://doi.acm.org/10.1145/1125451.1125659}, year = 2006 } @article{gedikli2010rating, author = {Gedikli, Fatih and Jannach, Dietmar}, interhash = {7a4e1b28558c54b576678146c5a614fe}, intrahash = {e7380137d10bd6a765897ea54bd05a31}, journal = {Systems and the Social Web at ACM }, title = {Rating items by rating tags}, year = 2010 } @inproceedings{heck2011testing, author = {Heck, Tamara and Peters, Isabella and Stock, Wolfgang G.}, booktitle = {Workshop on Recommender Systems and the Social Web (ACM RecSys'11)}, interhash = {d250a0eb45ca7c198d9cdb238802fd74}, intrahash = {8b68db4ae61ec5c97010fbec2ddaa6c6}, title = {Testing collaborative filtering against co-citation analysis and bibliographic coupling for academic author recommendation}, year = 2011 } @presentation{noauthororeditor, author = {leaong, Sheryl}, interhash = {94d316680af6c91206302e964f2d7918}, intrahash = {03ec5a6b30883646ee0c489630656b04}, title = {A survey of recommender systems for scientific papers}, year = 2012 } @inproceedings{McNee:2006:DLS:1180875.1180903, abstract = {If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.}, acmid = {1180903}, address = {New York, NY, USA}, author = {McNee, Sean M. and Kapoor, Nishikant and Konstan, Joseph A.}, booktitle = {Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {10.1145/1180875.1180903}, interhash = {24be686d042a3a4a710d9ff22dee0f2e}, intrahash = {7775150ca225770019bd94db9be5db40}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, numpages = {10}, pages = {171--180}, publisher = {ACM}, series = {CSCW '06}, title = {Don't look stupid: avoiding pitfalls when recommending research papers}, url = {http://doi.acm.org/10.1145/1180875.1180903}, year = 2006 } @phdthesis{bogers2009recommender, abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. }, address = {Tilburg, The Netherlands}, author = {Bogers, Toine}, interhash = {65b74dcabaa583a48469f3dec2ec1f62}, intrahash = {b02daac1201473600b7c8d2553865b4a}, month = dec, school = {Tilburg University}, title = {Recommender Systems for Social Bookmarking}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, year = 2009 } @article{Herlocker:2004:ECF:963770.963772, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.}, acmid = {963772}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, doi = {10.1145/963770.963772}, interhash = {f8a70731d983634ac7105896d101c9d2}, intrahash = {c3a659108a568db1fba183c680dd1fd2}, issn = {1046-8188}, issue = {1}, journal = {ACM Trans. Inf. Syst.}, month = {January}, numpages = {49}, pages = {5--53}, publisher = {ACM}, title = {Evaluating collaborative filtering recommender systems}, url = {http://doi.acm.org/10.1145/963770.963772}, volume = 22, year = 2004 } @inproceedings{marinho:ecml2009, abstract = {This paper describes our approach to the ECML/PKDD Discovery Challenge 2009. Our approach is a pure statistical model taking no content information into account. It tries to find latent interactions between users, items and tags by factorizing the observed tagging data. The factorization model is learned by the Bayesian Personal Ranking method (BPR) which is inspired by a Bayesian analysis of personalized ranking with missing data. To prevent overfitting, we ensemble the models over several iterations and hyperparameters. Finally, we enhance the top-n lists by estimating how many tags to recommend.}, address = {Bled, Slovenia}, author = {Rendle, Steffen and Schmidt-Thieme, Lars}, booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)}, editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert}, interhash = {8485850cde1a6b61971cac27fa867845}, intrahash = {ceed045a84e121fa37384f797306d30f}, issn = {1613-0073}, month = {September}, pages = {235--242}, publisher = {CEUR Workshop Proceedings}, title = {Factor Models for Tag Recommendation in BibSonomy}, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/}, volume = 497, year = 2009 } @article{landia2009personalised, author = {Landia, N. and Anand, S.S.}, interhash = {58558441386973e1ddd83f8129a54435}, intrahash = {77d75b4ce4f84d614b8ef42bd6f92616}, journal = {Recommender Systems \& the Social Web}, title = {{Personalised Tag Recommendation}}, url = {http://scholar.google.de/scholar.bib?q=info:GhmAwnjPyP0J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, year = 2009 } @article{10.1109/TKDE.2005.99, abstract = {This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.}, address = {Los Alamitos, CA, USA}, author = {Adomavicius, Gediminas and Tuzhilin, Alexander}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.99}, interhash = {42f7653127a823354d000ea95cf804be}, intrahash = {89ae9158a0b1f6b2f2fccbf4808acbf6}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, number = 6, pages = {734--749}, publisher = {IEEE Computer Society}, title = {Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions}, volume = 17, year = 2005 } @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 }