http://www.bibsonomy.org/user/jaeschke/taggingBibSonomy RSS feed for /user/jaeschke/tagging2013-05-18T11:28:01+02:00http://www.bibsonomy.org/bibtex/202d6739886a13180dd92fbb7243ab58b/jaeschkejaeschke2013-05-09T10:47:35+02:00analysis bookmarking collaborative folksonomy network tagging <span class="authorEditorList"><a href="/author/Angelova">Ralitsa Angelova</a>, <a href="/author/Lipczak">Marek Lipczak</a>, <a href="/author/Milios">Evangelos Milios</a>, and <a href="/author/Prałat">Paweł Prałat</a>. </span><em>Proceedings of the Mining Social Data Workshop MSoDa, </em><em>page 21--25. </em><em>ECAI 2008, </em>(<em>July 2008</em>)Thu May 09 10:47:35 CEST 2013Proceedings of the Mining Social Data Workshop (MSoDa)jul21--25Characterizing a social bookmarking and tagging network2008analysis bookmarking collaborative folksonomy network tagging 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. http://www.bibsonomy.org/bibtex/2d09b484b1036ca8273743cac1992dd7f/jaeschkejaeschke2012-10-12T09:08:53+02:00bookmarking cognition collaborative collective intelligence search social tagging web wiki <span class="authorEditorList"><a href="/author/Chi">Ed H. Chi</a>. </span><em>Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, </em><em>page 973--984. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Fri Oct 12 09:08:53 CEST 2012New York, NY, USAProceedings of the 2009 ACM SIGMOD International Conference on Management of data973--984Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason2009bookmarking cognition collaborative collective intelligence search social tagging web wiki We are experiencing a new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by systems like Wikipedia, twitter, and delicious.com, these environments are turning people into social information foragers and sharers. Groups interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."</p> <p>PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.</p> <p>Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.http://www.bibsonomy.org/bibtex/264bf590675a833770b7d284871435a8d/jaeschkejaeschke2012-09-06T21:54:12+02:002012 bookmarking collaborative folkrank myown recommender social tagging <span class="authorEditorList"><a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a>. </span><em>Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, </em><em>page 9--16. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>September 2012</em>)Thu Sep 06 21:54:12 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep9--16Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation 20122012 bookmarking collaborative folkrank myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.http://www.bibsonomy.org/bibtex/2b16dabcd7e17b673c34608ac820ce3c7/jaeschkejaeschke2012-09-06T21:52:34+02:002012 bookmarking collaborative folkrank myown recommender social tagging <span class="authorEditorList"><a href="/author/Landia">Nikolas Landia</a>, <a href="/author/Anand">Sarabjot Singh Anand</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, and <a href="/author/Mitzlaff">Folke Mitzlaff</a>. </span><em>Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, </em><em>page 1--8. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>September 2012</em>)Thu Sep 06 21:52:34 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep1--8Extending FolkRank with Content Data20122012 bookmarking collaborative folkrank myown recommender social tagging 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. 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.http://www.bibsonomy.org/bibtex/2a28959724af1907e7fc67a68e648c14c/jaeschkejaeschke2012-09-06T12:34:34+02:00bookmarking cloud collaborative everyaware geo location social tagging visualization <span class="authorEditorList"><a href="/author/Flohr">Oliver Flohr</a>. </span><em>Gottfried Wilhelm Leibniz Universität Hannover, </em><em>bachelor thesis, </em>(<em>August 2011</em>)Thu Sep 06 12:34:34 CEST 2012augExtraktion und Visualisierung ortsbezogener Informationen mit Tag-Cloudsbachelor thesis2011bookmarking cloud collaborative everyaware geo location social tagging visualization Informationen so aufzubereiten, dass sie für eine bestimmte Situation nützlich sind, ist eine große Herausforderung. In solchen Situationen soll ein Benutzer, wenn er sich an einem fremden Ort befindet, mit Hilfe des Android Smartphone interessante und wis- senswerte Informationen anzeigen lassen. Um dies bewerkstelligen zu können, muss es eine georeferenzierte Informationsquelle geben. Außerdem muss ein Konzept vor- handen sein, um diese Daten zu sammeln und so aufzubereiten, dass der Benutzer diese auch nützlich findet. Es muss eine Visualisierung dieser Daten geben, da der Platz zur Anzeige auf Smartphones sehr begrenzt ist. Als georeferenzierte Informationsquelle wird die Online-Enzyklopädie Wikipedia ge- nutzt, diese ist frei zugänglich und auch sehr umfassend. In dieser Arbeit wird das Konzept zur Sammlung und Aufbereitung von relevanten Daten behandelt. Zur In- formationsvisualisierung wird die Methode der Schlagwortwolke (engl. Tag-Cloud) verwendet. It is a major challenge to prepare useful information for a particular situation. In this situation an Android smartphone user wants to display interesting and important facts about an unknown place. To manage this task existence of a geo-referenced source of information has to be ensured. In order to collect and prepare this data a creation of concept is needed. Due to limited display space, it is necessary to construct a suitable visualization of this data. Wikipedia is used as a geo-referenced information resource, because it has open-access and it offers global geo-referenced information. This thesis covers the concept of col- lecting and preparing relevant data. To visualize information a tag cloud is used. http://www.bibsonomy.org/bibtex/210fe730b391b08031f3103f9cdbb6e1a/jaeschkejaeschke2012-06-29T12:41:22+02:00collaborative factorization folksonomy personalization recommender tag tagging tensor <span class="authorEditorList"><a href="/author/Rendle">Steffen Rendle</a>, and <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>. </span><em>Proceedings of the third ACM international conference on Web search and data mining, </em><em>page 81--90. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Fri Jun 29 12:41:22 CEST 2012New York, NY, USAProceedings of the third ACM international conference on Web search and data mining81--90Pairwise interaction tensor factorization for personalized tag recommendation2010collaborative factorization folksonomy personalization recommender tag tagging tensor 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.http://www.bibsonomy.org/bibtex/2684579385b3a4f90f5b41ce7c92ddb2a/jaeschkejaeschke2012-06-29T12:30:46+02:00collaborative folksonomy item recommender tagging <span class="authorEditorList"><a href="/author/Gemmell">Jonathan Gemmell</a>, <a href="/author/Schimoler">Thomas Schimoler</a>, <a href="/author/Mobasher">Bamshad Mobasher</a>, and <a href="/author/Burke">Robin Burke</a>. </span><em>E-Commerce and Web Technologies, </em><em>volume 61 of Lecture Notes in Business Information Processing, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2010</em>)Fri Jun 29 12:30:46 CEST 2012Berlin/HeidelbergE-Commerce and Web Technologies1--12Lecture Notes in Business Information ProcessingResource Recommendation in Collaborative Tagging Applications612010collaborative folksonomy item recommender tagging 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.http://www.bibsonomy.org/bibtex/2bdd023e357c901c749580d038b4f2059/jaeschkejaeschke2012-06-29T12:27:48+02:00collaborative content recommender tag tagging <span class="authorEditorList"><a href="/author/Musto">Cataldo Musto</a>, <a href="/author/Narducci">Fedelucio Narducci</a>, <a href="/author/Lops">Pasquale Lops</a>, and <a href="/author/de Gemmis">Marco de Gemmis</a>. </span><em>E-Commerce and Web Technologies, </em><em>volume 61 of Lecture Notes in Business Information Processing, </em><em>page 13--23. </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>2010</em>)Fri Jun 29 12:27:48 CEST 2012Berlin/HeidelbergE-Commerce and Web Technologies13--23Lecture Notes in Business Information ProcessingCombining Collaborative and Content-Based Techniques for Tag Recommendation.612010collaborative content recommender tag tagging The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender. http://www.bibsonomy.org/bibtex/26b1ff3b7b691b84288fb7122968134c4/jaeschkejaeschke2012-06-26T10:37:21+02:00bookmarking collaborative filtering folkrank recommender social tagging <span class="authorEditorList"><a href="/author/Gemmell">Jonathan Gemmell</a>, <a href="/author/Schimoler">Thomas R. Schimoler</a>, <a href="/author/Christiansen">Laura Christiansen</a>, and <a href="/author/Mobasher">Bamshad Mobasher</a>. </span><em>ACM RecSys&#039;09 Workshop on Recommender Systems and the Social Web, </em><em>volume 532 of CEUR-WS.org, </em><em>page 17--24. </em>(<em>October 2009</em>)Tue Jun 26 10:37:21 CEST 2012ACM RecSys'09 Workshop on Recommender Systems and the Social Weboct17--24CEUR-WS.orgImproving Folkrank With Item-Based Collaborative Filtering5322009bookmarking collaborative filtering folkrank recommender social tagging Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.http://www.bibsonomy.org/bibtex/2e5a4b67ed6173e9645aab321019efd74/jaeschkejaeschke2012-03-14T11:49:48+01:002011 bookmarking folksonomy letor myown ranking social tagging <span class="authorEditorList"><a href="/author/Navarro Bullock">Beate Navarro Bullock</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Hotho">Andreas Hotho</a>. </span><em>Proceedings of the ACM WebSci Conference, </em><em>page 1--4. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>June 2011</em>)Wed Mar 14 11:49:48 CET 2012New York, NY, USAProceedings of the ACM WebSci Conferencejun1--4Tagging data as implicit feedback for learning-to-rank20112011 bookmarking folksonomy letor myown ranking social tagging 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.http://www.bibsonomy.org/bibtex/242773258c36ccf2f59749991518d1784/jaeschkejaeschke2012-03-10T14:29:45+01:00collaborative filtering folksonomy item recommender social tagging <span class="authorEditorList"><a href="/author/Parra">Denis Parra</a>, and <a href="/author/Brusilovsky">Peter Brusilovsky</a>. </span><em>Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web, </em><em>volume 467 of CEUR Workshop Proceedings, </em>(<em>June 2009</em>)Sat Mar 10 14:29:45 CET 2012Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social WebjunCEUR Workshop ProceedingsEvaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike4672009collaborative filtering folksonomy item recommender social tagging 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. http://www.bibsonomy.org/bibtex/2ec592568ca4a9f6b2ebaf41816af1ebc/jaeschkejaeschke2012-03-10T14:20:37+01:00collaborative folksonomy item recommender social tagging <span class="authorEditorList"><a href="/author/Lee">Danielle H. Lee</a>, and <a href="/author/Brusilovsky">Peter Brusilovsky</a>. </span><em>Proceedings of the fourth ACM conference on Recommender systems, </em><em>page 221--224. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Sat Mar 10 14:20:37 CET 2012New York, NY, USAProceedings of the fourth ACM conference on Recommender systems221--224Using self-defined group activities for improving recommendations in collaborative tagging systems2010collaborative folksonomy item recommender social tagging 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.http://www.bibsonomy.org/bibtex/2d335b38783be877ea4e000e0c332cef4/jaeschkejaeschke2012-03-10T14:03:54+01:00folksonomy item recommender social tagging <span class="authorEditorList"><a href="/author/Pera">Maria Soledad Pera</a>, and <a href="/author/Ng">Yiu-Kai Ng</a>. </span><em>Proceedings of the 20th ACM international conference on Information and knowledge management, </em><em>page 2133--2136. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2011</em>)Sat Mar 10 14:03:54 CET 2012New York, NY, USAProceedings of the 20th ACM international conference on Information and knowledge management2133--2136A personalized recommendation system on scholarly publications2011folksonomy item recommender social tagging Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.http://www.bibsonomy.org/bibtex/2ac6d55bacc85f75a4711a1c48526dfd6/jaeschkejaeschke2012-03-10T13:53:40+01:00folksonomy item recommender semantics tagging <span class="authorEditorList"><a href="/author/Cantador">Iván Cantador</a>, <a href="/author/Bellogín">Alejandro Bellogín</a>, <a href="/author/Fernández-Tobías">Ignacio Fernández-Tobías</a>, and <a href="/author/López-Hernández">Sergio López-Hernández</a>. </span><em>E-Commerce and Web Technologies, </em><em>volume 85 of Lecture Notes in Business Information Processing, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2011</em>)Sat Mar 10 13:53:40 CET 2012Berlin/HeidelbergE-Commerce and Web Technologies101--113Lecture Notes in Business Information ProcessingSemantic Contextualisation of Social Tag-Based Profiles and Item Recommendations852011folksonomy item recommender semantics tagging We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.http://www.bibsonomy.org/bibtex/2fd9284874d7896d3aee8a9641efe368a/jaeschkejaeschke2012-03-09T14:15:01+01:00folksonomy item recommender social tagging topic <span class="authorEditorList"><a href="/author/Wartena">Christian Wartena</a>, and <a href="/author/Wibbels">Martin Wibbels</a>. </span><em>Advances in Information Retrieval, </em><em>volume 6611 of Lecture Notes in Computer Science, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2011</em>)Fri Mar 09 14:15:01 CET 2012Berlin/HeidelbergAdvances in Information Retrieval43--54Lecture Notes in Computer ScienceImproving Tag-Based Recommendation by Topic Diversification66112011folksonomy item recommender social tagging topic Collaborative tagging has emerged as a mechanism to describe items in large on-line collections. Tags are assigned by users to describe and find back items, but it is also tempting to describe the users in terms of the tags they assign or in terms of the tags of the items they are interested in. The tag-based profile thus obtained can be used to recommend new items. If we recommend new items by computing their similarity to the user profile or to all items seen by the user, we run into the risk of recommending only neutral items that are a bit relevant for each topic a user is interested in. In order to increase user satisfaction many recommender systems not only optimize for accuracy but also for diversity. Often it is assumed that there exists a trade-off between accuracy and diversity. In this paper we introduce topic aware recommendation algorithms. Topic aware algorithms first detect different interests in the user profile and then generate recommendations for each of these interests. We study topic aware variants of three tag based recommendation algorithms and show that each of them gives better recommendations than their base variants, both in terms of precision and recall and in terms of diversity.http://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/2f022e60c5928e01c701d7ec539ec221b/jaeschkejaeschke2011-12-21T22:52:09+01:00bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank <span class="authorEditorList"><a href="/author/Kim">Heung-Nam Kim</a>, and <a href="/author/El Saddik">Abdulmotaleb El Saddik</a>. </span><em>Proceedings of the fifth ACM conference on Recommender systems, </em><em>page 45--52. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2011</em>)Wed Dec 21 22:52:09 CET 2011New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52Personalized PageRank vectors for tag recommendations: inside FolkRank2011bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank 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.http://www.bibsonomy.org/bibtex/2d44d1c9a48f5b676388ffbc90c7577ba/jaeschkejaeschke2011-12-05T18:36:22+01:00collaborative folksonomy information social tagging theory <span class="authorEditorList"><a href="/author/Chi">Ed H. Chi</a>, and <a href="/author/Mytkowicz">Todd Mytkowicz</a>. </span><em>Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page 81--88. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Mon Dec 05 18:36:22 CET 2011New York, NY, USAProceedings of the nineteenth ACM conference on Hypertext and hypermedia81--88Understanding the efficiency of social tagging systems using information theory2008collaborative folksonomy information social tagging theory Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.