@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 }