@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 } @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}, vgwort = {30}, volume = 32, year = 2011 } @incollection{marinho2011social, abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. 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 STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.}, address = {New York}, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, doi = {10.1007/978-0-387-85820-3_19}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1}, intrahash = {708be7b5c269bd3a9d3d2334f858d52d}, isbn = {978-0-387-85820-3}, pages = {615--644}, publisher = {Springer}, title = {Social Tagging Recommender Systems}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, vgwort = {50}, year = 2011 } @article{atzmueller2011enhancing, abstract = {Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system.}, address = {München}, author = {Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd}, doi = {10.1524/itit.2011.0631}, interhash = {e57bff1f73b74e6f1fe79e4b40956c35}, intrahash = {b96a6cf5d9999ca9063b7d7cd229e50d}, issn = {1611-2776}, journal = {Information Technology}, month = may, number = 3, pages = {101--107}, publisher = {Oldenbourg Wissenschaftsverlag}, title = {Enhancing Social Interactions at Conferences}, url = {http://www.oldenbourg-link.com/doi/abs/10.1524/itit.2011.0631}, vgwort = {22}, volume = 53, year = 2011 } @proceedings{valtchev2011formal, abstract = {The present volume features a selection of the papers presented at the 9th International Conference on Formal Concept Analysis (ICFCA 2011). Over the years, the ICFCA conference series has grown into the premier forum for dissemination of research on topics from formal concept analysis (FCA) theory and applications, as well as from the related fields of lattices and partially ordered structures. FCA is a multi-disciplinary field with strong roots in the mathematical theory of partial orders and lattices, with tools originating in computer science and artificial intelligence. FCA emerged in the early 1980s from efforts to restructure lattice theory to promote better communication between lattice theorists and potential users of lattice-based methods for data management. Initially, the central theme was the mathematical formalization of concept and conceptual hierarchy. Since then, the field has developed into a constantly growing research area in its own right with a thriving theoretical community and an increasing number of applications in data and knowledge processing including disciplines such as data visualization, information retrieval, machine learning, software engineering, data analysis, data mining, social networks analysis, etc. ICFCA 2011 was held from May 2 to May 6, 2011, in Nicosia, Cyprus. The program committee received 49 high-quality submissions that were subjected to a highly competitive selection process. Each paper was reviewed by three referees (exceptionally two or four). After a first round, some papers got a definitive acceptance status, while others got accepted conditionally to improvements in their content. The latter got to a second round of reviewing. The overall outcome was the acceptance of 16 papers as regular ones for presentation at the conference and publication in this volume. Another seven papers have still been assessed as valuable for discussion at the conference and were therefore collected in the supplementary proceedings. The regular papers presented hereafter cover advances on a wide range of subjects from FCA and related fields. A first group of papers tackled mathematical problems within the FCA field. A subset thereof focused on factor identification within the incidence relation or its lattice representation (papers by Glodeanu and by Krupka). The remainder of the group proposed characterizations of particular classes of ordered structures (papers by Doerfel and by Meschke et al.). A second group of papers addressed algorithmic problems from FCA and related fields. Two papers approached their problems from an algorithmic complexity viewpoint (papers by Distel and by Babin and Kuznetsov) while the final paper in this group addressed algorithmic problems for general lattices, i.e., not represented as formal contexts, with an FCA-based approach (work by Balcázar and Tîrnăucă). A third group studied alternative approaches for extending the expressive power of the core FCA, e.g., by generalizing the standard one-valued attributes to attributes valued in algebraic rings (work by González Calabozo et al.), by introducing pointer-like attributes, a.k.a. links (paper by Kötters), or by substituting set-shaped concept intents with modal logic expressions (paper by Soldano and Ventos). A fourth group focused on data mining-oriented aspects of FCA: agreement lattices in structured data mining (paper by Nedjar et al.), triadic association rule mining (work by Missaoui and Kwuida) and bi-clustering of numerical data (Kaytoue et al.). An addional paper shed some initial light on a key aspect of FCA-based data analysis and mining, i.e., the filtering of interesting concepts (paper by Belohlavek and Macko). Finally, a set of exciting applications of both basic and enhanced FCA frameworks to practical problems have beed described: in analysis of gene expression data (the already mentioned work by González Calabozo et al.), in web services composition (paper by Azmeh et al.) and in browsing and retrieval of structured data (work by Wray and Eklund). This volume also contains three keynote papers submitted by the invited speakers of the conference. All these contributions constitute a volume of high quality which is the result of the hard work done by the authors, the invited speakers and the reviewers. We therefore wish to thank the members of the Program Committee and of the Editorial Board whose steady involvement and professionalism helped a lot. We would also like to acknowledge the participation of all the external reviewers who sent many valuable comments. Kudos also go to EasyChair for having made the reviewing/editing process a real pleasure. Special thanks go to the Cyprus Tourism Organisation for sponsoring the conference and to the University of Nicosia for hosting it. Finally we wish to thank the Conference Chair Florent Domenach and his colleagues from the Organization Committee for the mountains of energy they put behind the conference organization process right from the beginning in order to make it a total success. We would also like to express our gratitude towards Dr. Peristianis, President of the University of Nicosia, for his personal support. }, address = {Berlin/Heidelberg}, doi = {10.1007/978-3-642-20514-9_2}, editor = {Valtchev, Petko and Jäschke, Robert}, interhash = {a7fd7ebbb14eacc605ff61cf2759cb06}, intrahash = {afd54a24a2eeca1a07f811bd89800d28}, isbn = {978-3-642-20513-2}, month = may, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, title = {Formal Concept Analysis}, url = {http://www.springer.com/computer/ai/book/978-3-642-20513-2}, vgwort = {452}, volume = 6628, year = 2011 }