BibSonomyburst/user/jaeschke/recommenderBibSonomy RSS feed for /user/jaeschke/recommender2012-02-16T17:17:22+01:00http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/jaeschkejaeschke2012-02-13T12:52:23+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Mon Feb 13 12:52:23 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging 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.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/jaeschkejaeschke2012-02-06T13:47:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 13:47:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/267b105a941f0a557c6d457447625cbfb/jaeschkejaeschke2012-01-09T13:46:31+01:00recommender survey tag tagging <span class="authorEditorList"><a href="/author/Zhang">Zi-Ke Zhang</a>, <a href="/author/Zhou">Tao Zhou</a>, and <a href="/author/Zhang">Yi-Cheng Zhang</a> </span><em>Journal of Computer Science and Technology</em> <em>26(5):767--777</em> (<em>2011</em>)Mon Jan 09 13:46:31 CET 2012Journal of Computer Science and Technology5767--777Tag-Aware Recommender Systems: A State-of-the-Art Survey262011recommender survey tag tagging In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.http://www.bibsonomy.org/bibtex/21f1be967aed57e6e42a5d99ca98584cd/jaeschkejaeschke2011-12-12T09:02:50+01:00collaborative filtering recommender <span class="authorEditorList"><a href="/author/Takács">Gábor Takács</a>, <a href="/author/Pilászy">István Pilászy</a>, <a href="/author/Németh">Bottyán Németh</a>, and <a href="/author/Tikk">Domonkos Tikk</a> </span><em>Journal of Machine Learning Research</em> (<em>June 2009</em>)Mon Dec 12 09:02:50 CET 2011Journal of Machine Learning Researchjun623--656Scalable Collaborative Filtering Approaches for Large Recommender Systems102009collaborative filtering recommender The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.http://www.bibsonomy.org/bibtex/241dbb2c9f71440c9aa402f8966117979/jaeschkejaeschke2011-11-01T08:08:16+01:002011 collaborative myown recommender social tagging web <span class="authorEditorList"><a href="/author/Burke">Robin Burke</a>, <a href="/author/Gemmell">Jonathan Gemmell</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Jäschke">Robert Jäschke</a> </span><em>AI Magazine</em> <em>32(3):46--56</em> (<em>2011</em>)Tue Nov 01 08:08:16 CET 2011AI Magazine346--56Recommendation in the Social Web3220112011 collaborative myown recommender social tagging web 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.http://www.bibsonomy.org/bibtex/23a2c3898216376eab27848a7f147ee51/jaeschkejaeschke2011-08-22T10:33:43+02:00collaborative folksonomy random recommender tagging walk <span class="authorEditorList"><a href="/author/Konstas">Ioannis Konstas</a>, <a href="/author/Stathopoulos">Vassilios Stathopoulos</a>, and <a href="/author/Jose">Joemon M. Jose</a> </span><em>Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, </em><em>page 195--202. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Mon Aug 22 10:33:43 CEST 2011New York, NY, USAProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval195--202SIGIR '09On social networks and collaborative recommendation2009collaborative folksonomy random recommender tagging walk Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.http://www.bibsonomy.org/bibtex/2fe43da7e093f06c36010358724d03b7b/jaeschkejaeschke2011-07-04T11:57:00+02:00collaborative folksonomy media personalization recommender social tagging <span class="authorEditorList"><a href="/author/Clements">M. Clements</a> </span><em>Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007, </em>(<em>August 2007</em>)Mon Jul 04 11:57:00 CEST 2011Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007augPersonalization of Social Media2007collaborative folksonomy media personalization recommender social tagging This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify many more opportunities. We propose a collaborative model that combines collaborative filtering and information retrieval techniques in order to assists the user to achieve these tasks. Based only on the user's tags, this personalization model assumes that a user's tags identify this user's taste. Because many users do not only tag the content that matches their taste, we propose an evaluating experiment that shows if rating information can be used to adjust the users' taste profiles. This experiment is one of the steps to advance to a completely personalized model, integrating user preference, content annotations and people relations.http://www.bibsonomy.org/bibtex/27775150ca225770019bd94db9be5db40/jaeschkejaeschke2011-07-04T11:23:43+02:00recommender study user <span class="authorEditorList"><a href="/author/McNee">Sean M. McNee</a>, <a href="/author/Kapoor">Nishikant Kapoor</a>, and <a href="/author/Konstan">Joseph A. Konstan</a> </span><em>Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, </em><em>page 171--180. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Mon Jul 04 11:23:43 CEST 2011New York, NY, USAProceedings of the 2006 20th anniversary conference on Computer supported cooperative work171--180CSCW '06Don't look stupid: avoiding pitfalls when recommending research papers2006recommender study user 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.http://www.bibsonomy.org/bibtex/2fa706dfc6865c0abcd6b3920e3e786ca/jaeschkejaeschke2011-05-26T13:40:55+02:00challenge movie recommender recsys <span class="authorEditorList"><a href="/author/Said">Alan Said</a>, <a href="/author/Berkovsky">Shlomo Berkovsky</a>, and <a href="/author/Luca">Ernesto W. De Luca</a> </span><em>Proceedings of the Workshop on Context-Aware Movie Recommendation, </em><em>page 2--6. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Thu May 26 13:40:55 CEST 2011New York, NY, USAProceedings of the Workshop on Context-Aware Movie Recommendation2--6Putting things in context: Challenge on Context-Aware Movie Recommendation2010challenge movie recommender recsys The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event on Context-Awareness in Recommender Systems at the 2010 ACM Recommender Systems conference. The challenge focused on three context-aware recommendation tasks: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real world online movie recommendation communities and competed against each other for obtaining the highest recommendation accuracy for each task. The datasets contained contextual features, such as mood, plot annotation, social network, and comments, normally not available in movie recommendation datasets. Over 40 teams from 20 countries participated in the challenge. Their participation was summarized by 10 papers accepted to the CAMRa workshop.http://www.bibsonomy.org/bibtex/216ae86f12fc8496399bfb3b6f3181113/jaeschkejaeschke2011-05-26T11:59:34+02:00challenge netflix recommender <span class="authorEditorList"><a href="/author/Bell">Robert M. Bell</a>, and <a href="/author/Koren">Yehuda Koren</a> </span><em>SIGKDD Explorations Newsletter</em> <em>9(2):75--79</em> (<em>December 2007</em>)Thu May 26 11:59:34 CEST 2011New York, NY, USASIGKDD Explorations Newsletterdec275--79Lessons from the Netflix prize challenge92007challenge netflix recommender This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.http://www.bibsonomy.org/bibtex/298034c615577fd3558fd326fbe03f894/jaeschkejaeschke2011-05-26T11:27:10+02:00collaborative recommender social tag tagging <span class="authorEditorList"><a href="/author/Rae">Adam Rae</a>, <a href="/author/Sigurbjörnsson">Börkur Sigurbjörnsson</a>, and <a href="/author/van Zwol">Roelof van Zwol</a> </span><em>Adaptivity, Personalization and Fusion of Heterogeneous Information, </em><em>page 92--99. </em><em>Paris, France, </em><em>Le Centre De Hautes Etudes Internationales d&#039;Informatique Documentaire, </em>(<em>2010</em>)Thu May 26 11:27:10 CEST 2011Paris, FranceAdaptivity, Personalization and Fusion of Heterogeneous Information92--99RIAO '10Improving tag recommendation using social networks2010collaborative recommender social tag tagging In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel.</p> <p>For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour.http://www.bibsonomy.org/bibtex/28e5fdf385f7bae639ca978259d9ec8de/jaeschkejaeschke2011-05-26T11:15:03+02:00collaborative recommender social tagging <span class="authorEditorList"><a href="/author/Song">Yang Song</a>, <a href="/author/Zhang">Lu Zhang</a>, and <a href="/author/Giles">C. Lee Giles</a> </span><em>Transactions on the Web</em> <em>5(1):1--31</em> (<em>February 2011</em>)Thu May 26 11:15:03 CEST 2011New York, NY, USATransactions on the Webfeb11--31Automatic tag recommendation algorithms for social recommender systems52011collaborative recommender social tagging The emergence of Web 2.0 and the consequent success of social network Web sites such as Del.icio.us and Flickr introduce us to a new concept called social bookmarking, or tagging. Tagging is the action of connecting a relevant user-defined keyword to a document, image, or video, which helps the user to better organize and share their collections of interesting stuff. With the rapid growth of Web 2.0, tagged data is becoming more and more abundant on the social network Web sites. An interesting problem is how to automate the process of making tag recommendations to users when a new resource becomes available.</p> <p>In this article, we address the issue of tag recommendation from a machine learning perspective. From our empirical observation of two large-scale datasets, we first argue that the user-centered approach for tag recommendation is not very effective in practice. Consequently, we propose two novel document-centered approaches that are capable of making effective and efficient tag recommendations in real scenarios. The first, graph-based, method represents the tagged data in two bipartite graphs, (document, tag) and (document, word), then finds document topics by leveraging graph partitioning algorithms. The second, prototype-based, method aims at finding the most representative documents within the data collections and advocates a sparse multiclass Gaussian process classifier for efficient document classification. For both methods, tags are ranked within each topic cluster/class by a novel ranking method. Recommendations are performed by first classifying a new document into one or more topic clusters/classes, and then selecting the most relevant tags from those clusters/classes as machine-recommended tags.</p> <p>Experiments on real-world data from Del.icio.us, CiteULike, and BibSonomy examine the quality of tag recommendation as well as the efficiency of our recommendation algorithms. The results suggest that our document-centered models can substantially improve the performance of tag recommendations when compared to the user-centered methods, as well as topic models LDA and SVM classifiers.http://www.bibsonomy.org/bibtex/2efb6cb6220dfdd1e3d9ca4894e9f1459/jaeschkejaeschke2011-05-26T11:06:31+02:00collaborative recommender social survey tagging <span class="authorEditorList"><a href="/author/Milicevic">Aleksandra Milicevic</a>, <a href="/author/Nanopoulos">Alexandros Nanopoulos</a>, and <a href="/author/Ivanovic">Mirjana Ivanovic</a> </span><em>Artificial Intelligence Review</em> <em>33(3):187--209</em> (<em>January 2010</em>)Thu May 26 11:06:31 CEST 2011Artificial Intelligence Reviewjan3187--209Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions332010collaborative recommender social survey tagging Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.http://www.bibsonomy.org/bibtex/2708be7b5c269bd3a9d3d2334f858d52d/jaeschkejaeschke2011-05-13T11:38:14+02:002011 collaborative myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">Leandro Balby Marinho</a>, <a href="/author/Nanopoulos">Alexandros Nanopoulos</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Symeonidis">Panagiotis Symeonidis</a> </span><em>Recommender Systems Handbook, </em><em>Springer, </em><em>New York, </em>(<em>2011</em>)Fri May 13 11:38:14 CEST 2011New YorkRecommender Systems Handbook615--644Social Tagging Recommender Systems20112011 collaborative myown recommender social tagging 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.http://www.bibsonomy.org/bibtex/20dad64a7e8e7fbfe51a4fc22ee533a1a/jaeschkejaeschke2011-05-09T11:44:55+02:002010 collaborative folksonomy recommender tagging <span class="authorEditorList"><a href="/author/Lipczak">Marek Lipczak</a>, and <a href="/author/Milios">Evangelos Milios</a> </span><em>Proceedings of the fourth ACM conference on Recommender systems, </em><em>page 167--174. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Mon May 09 11:44:55 CEST 2011New York, NY, USAProceedings of the fourth ACM conference on Recommender systems167--174RecSys '10Learning in efficient tag recommendation20102010 collaborative folksonomy recommender tagging The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing efficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems confirmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system efficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources.http://www.bibsonomy.org/bibtex/29db90c2ff04f514ada9f6b50fde46065/jaeschkejaeschke2011-01-27T15:14:23+01:002011 analysis collaborative concept fca folksonomy formal myown recommender tag tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a> </span><em>Dissertationen zur Künstlichen Intelligenz </em><em>Akademische Verlagsgesellschaft AKA, </em><em>Heidelberg, Germany, </em>(<em>January 2011</em>)Thu Jan 27 15:14:23 CET 2011Heidelberg, GermanyjanDissertationen zur Künstlichen IntelligenzFormal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems33220112011 analysis collaborative concept fca folksonomy formal myown recommender tag tagging One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags are used for navigation, finding resources, and serendipitous browsing and thus provide an immediate benefit for the user. By now, several systems for tagging photos, web links, publication references, videos, etc. have attracted millions of users which in turn annotated countless resources. Tagging gained so much popularity that it spread into other applications like web browsers, software packet managers, and even file systems. Therefore, the relevance of the methods presented in this thesis goes beyond the Web 2.0. The conceptual structure underlying collaborative tagging systems is called folksonomy. It can be represented as a tripartite hypergraph with user, tag, and resource nodes. Each edge of the graph expresses the fact that a user annotated a resource with a tag. This social network constitutes a lightweight conceptual structure that is not formalized, but rather implicit and thus needs to be extracted with knowledge discovery methods. In this thesis a new data mining task – the mining of all frequent tri-concepts – is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. Extending the theory of triadic Formal Concept Analysis, we provide a formal definition of the problem, and present an efficient algorithm for its solution. We show the applicability of our approach on three large real-world examples and thereby perform a conceptual clustering of two collaborative tagging systems. Finally, we introduce neighborhoods of triadic concepts as basis for a lightweight visualization of tri-lattices. The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by our research group. Besides being a useful tool for many scientists, it provides interested researchers a basis for the evaluation and integration of their knowledge discovery methods. This thesis introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features. Furthermore, BibSonomy is used as foundation for evaluating and integrating some of the discussed approaches. Collaborative tagging systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In this thesis we evaluate and compare several recommendation algorithms on large-scale real-world datasets: an adaptation of user-based Collaborative Filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag co-occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag co-occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We demonstrate how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. Furthermore, we show how to integrate recommendation methods into a real tagging system, record and evaluate their performance by describing the tag recommendation framework we developed for 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. We also present an evaluation of the framework which demonstrates its power. The folksonomy graph shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Clicklogs of web search engines can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large folksonomy snapshot and on query logs of two large search engines. We find that all of the three datasets exhibit similar structural properties and thus conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of collaborative tagging users is driven by similar dynamics. In this thesis we further transfer the folksonomy paradigm to the Social Semantic Desktop – a new model of computer desktop that uses Semantic Web technologies to better link information items. There we apply community support methods to the folksonomy found in the network of social semantic desktops. Thus, we connect knowledge discovery for folksonomies with semantic technologies. Alltogether, the research in this thesis is centered around collaborative tagging systems and their underlying datastructure – folksonomies – and thereby paves the way for the further dissemination of this successful knowledge management paradigm. http://www.bibsonomy.org/bibtex/2955bcf14f3272ba6eaf3dadbef6c0b10/jaeschkejaeschke2011-01-27T12:01:46+01:002008 myown recommender tag top webzu <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Marinho">Leandro Marinho</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>AI Communications</em> <em>21(4):231--247</em> (<em>December 2008</em>)Thu Jan 27 12:01:46 CET 2011AmsterdamAI Communicationsdec4231--247Tag Recommendations in Social Bookmarking Systems2120082008 myown recommender tag top webzu Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. http://www.bibsonomy.org/bibtex/221fdf612ba6b356fb1b311fc9369f32d/jaeschkejaeschke2011-01-27T11:58:48+01:002009 bibsonomy conference framework myown recommender recsys <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Eisterlehner">Folke Eisterlehner</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>RecSys &#039;09: Proceedings of the third ACM Conference on Recommender Systems, </em><em>page 369--372. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Thu Jan 27 11:58:48 CET 2011New York, NY, USARecSys '09: Proceedings of the third ACM Conference on Recommender Systems369--372Testing and Evaluating Tag Recommenders in a Live System20092009 bibsonomy conference framework myown recommender recsys 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.http://www.bibsonomy.org/bibtex/2a1c3f0b4a9bd5273ffd298128590598a/jaeschkejaeschke2010-11-10T17:28:33+01:00collaborative recommender resource tagging <span class="authorEditorList"><a href="/author/Bogers">Toine Bogers</a>, and <a href="/author/van den Bosch">Antal van den Bosch</a> </span><em>Proceedings of the 2008 ACM Conference on Recommender Systems, </em><em>page 287--290. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Nov 10 17:28:33 CET 2010New York, NY, USAProceedings of the 2008 ACM Conference on Recommender Systems287--290Recommending scientific articles using citeulike2008collaborative recommender resource tagging We describe the use of the social reference management website CiteULike for recommending scientific articles to users, based on their reference library. We test three different collaborative filtering algorithms, and find that user-based filtering performs best. A temporal analysis of the data indexed by CiteULike shows that it takes about two years for the cold-start problem to disappear and recommendation performance to improve.http://www.bibsonomy.org/bibtex/2aea6ea6f248a233c9609a93a2e1ee7fa/jaeschkejaeschke2010-09-09T14:44:25+02:00folksonomy recommender survey tagging <span class="authorEditorList"><a href="/author/Dattolo">A. Dattolo</a>, <a href="/author/Ferrara">F. Ferrara</a>, and <a href="/author/Tasso">C. Tasso</a> </span><em>Proc. of the 3rd International Conference on Human System Interaction - HSI&#039;2010, </em><em>page 548--555. </em><em>IEEE Press, </em>(<em>May 2010</em>)Thu Sep 09 14:44:25 CEST 2010Proc. of the 3rd International Conference on Human System Interaction - HSI'2010may548--555The role of tags for recommendation: a survey.2010folksonomy recommender survey tagging Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other peers for browsing available resources. However, due to the absence of rules for managing the tagging process, and to the lack of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications dop not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing. For this reason researchers are modeling innovative recommender systems capable to better support tagging, browsing, and searching for new resources. This paper is a survey which discusses the role of tags in recommender systems: starting from social tagging systems, we analyze various techniques for suggesting content and we introduce the approaches exploited for proposing tags for classifying resources, considering both personalized and not-personalized recommendation.