BibSonomy publications for /tag/ws07Wed Aug 13 11:00:11 CEST 2008Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007recommender tagging_proposal tagging_convergence content bookmarking ws07 projekt classification seminar tagging kde This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.Sun Jan 13 19:56:15 CET 2008Semantic Web Workshopconf/sww/2002CEUR Workshop ProceedingsExploiting Synergy Between Ontologies and Recommender Systems.552002recommender projekt synergy ontology ws07 knowledge kde seminar Sun Jan 13 19:48:53 CET 2008New York, NY, USAK-CAP '01: Proceedings of the 1st international conference on Knowledge capture100--107Capturing knowledge of user preferences: ontologies in recommender systems2001recommender ws07 projekt ontologies knowledge seminar user kde Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.Sun Jan 13 19:35:05 CET 2008Applications of Data Mining to Electronic Commerce1/211-32Personalization of Supermarket Product Recommendations52001rules seminar recommender ws07 kde projekt clustering Sun Jan 13 14:38:34 CET 2008Papers from the AAAI Workshop, AAAI Technical Report WS-00-0474-77Knowledge Based Recommender Systems Using Explicit User Models2000user projekt seminar ws07 knowledge recommender kde Sun Jan 13 14:37:46 CET 2008Encyclopedia of Library and Information Science32Knowledge--Based Recommender Systems692000ws07 projekt seminar knowledge recommender kde Tue Jan 08 16:52:49 CET 2008Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007content recommender tagging seminar ws07 bookmarking classification projekt kde This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.Tue Jan 08 16:43:48 CET 2008New York, NY, USAIUI '00: Proceedings of the 5th International Conference on Intelligent User Interfaces106--112{Mining Navigation History for Recommendation.}2000rules learning navigation ws07 kde seminar history recommender projekt Wed Dec 12 11:10:18 CET 2007cs.DS/0202039Generalized Cores2002projekt generalized ws07 kcore graph recommender analysis network kde seminar core Cores are, besides connectivity components, one among few concepts that provides us with efficient decompositions of large graphs and networks. In the paper a generalization of the notion of core of a graph based on vertex property function is presented. It is shown that for the local monotone vertex property functions the corresponding cores can be determined in $O(m \max (\Delta, \log n))$ time.Wed Nov 21 21:48:52 CET 2007Data Science and Classification251--260kNN Versus SVM in the Collaborative Filtering Framework2006learning recommender projekt seminar classification svm kde ws07 knn We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used forthe collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Sincecollaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm(such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the otherhand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We concludethat the quality of collaborative filtering recommendations is highly dependent on the quality of the data. Furthermore, wecan see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level ofsparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperforms kNN.Wed Nov 21 21:46:43 CET 2007San Francisco, CA, USAICML '01: Proceedings of the Eighteenth International Conference on Machine Learning314--321Collaborative Learning and Recommender Systems2001seminar recommender projekt classification ws07 learning kde Wed Nov 21 21:46:08 CET 2007San Francisco, CA, USAICML '98: Proceedings of the Fifteenth International Conference on Machine Learning46--54Learning Collaborative Information Filters1998ws07 projekt kde seminar learning classification recommender Wed Nov 21 21:44:17 CET 2007Menlo Park, CA, USAAAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence714--720Recommendation as classification: using social and content-based information in recommendation1998recommender learning classification ws07 projekt kde seminar Thu Nov 01 12:26:38 CET 2007Knowledge and Data Engineering, IEEE Transactions on734- 749Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions172005projekt extension survey recommender seminar kde ws07 This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.Thu Nov 01 12:18:20 CET 2007HeidelbergThe Semantic Web: Research and Applications411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking40112006recommender ir folkrank seminar tagging projekt ws07 kde folksonomy Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.Thu Nov 01 12:13:42 CET 2007Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, ProceedingsDBLP:conf/pkdd/2007506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007folksonomy pagerank ws07 projekt seminar kde recommender tagging Thu Nov 01 12:09:31 CET 2007Houston, Texas, USAProceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005378-385Compound Classification Models for Recommender Systems.2005classification projekt seminar learning ws07 recommender kde Thu Nov 01 12:04:41 CET 2007IWP-Information Wissenschaft & Praxis5265-276Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art582007folksonomy survey projekt kde seminar ws07 recommender Empfehlungssysteme tragen Inhalte individuell an Nutzer im WWW heran, basierend auf deren konkreten Bedürfnissen, Vorlieben und Interessen. Solche Systeme können Produkte, Services, Nutzer (mit analogen Interessen) uvm. vorschlagen und stellen damit – gerade im Web 2.0-Zeitalter – eine besondere Form der Personalisierung sowie des social networking dar. Damit bieten Empfehlungssysteme Anbietern im ECommerce einen entscheidenden Marktvorteil, weshalb die Auswertung der Kundendaten bei großen Firmen wie Amazon, Google oder Ebay eine hohe Priorität besitzt. Aus diesem Grund wird im vorliegenden Artikel auf die Ansätze von Empfehlungssystemen, welche auf unterschiedliche Weise die Bedürfnisse des Nutzers aufgreifen bzw. „vorausahnen“ und ihm Vorschläge (aus verschiedenen Bereichen) unterbreiten können, eingegangen. Der Artikel liefert eine Definition und Darstellung der Arbeitsweisen von Empfehlungssystemen. Dabei werden die verschiedenen Methodiken jener Dienste vergleichend erläutert, um ihre jeweiligen Vor- und Nachteile deutlich zu machen. Außerdem wird der Ontologie- und Folksonomy-Einsatz innerhalb von Empfehlungssystemen beleuchtet, um Chancen und Risiken der Anwendung von Methoden der Wissensrepräsentation für zukünftige Forschungsarbeiten einschätzen zu können. Recommender Systems in an Information Science View – The State of the Art Recommender systems offer content individually to users in the WWW, based on their concrete needs, preferences and interests. Those systems can propose products, services, users (with analogous interests), etc.) and represent a special form of personalisation as well as of social networking – exactly in the Web 2.0 age. Recommender systems offer e.g. suppliers in the e-commerce a crucial market advantage. So, the evaluation of the customer data has high priority at big companies like Amazon, Google or Ebay. For this reason we engaged in recommender systems, which take up the user’s needs in different ways, to “anticipate“ needs and make suggestions (from different areas) to the user. This review article achieves a definition and representation of operations and methods of recommender systems. Exactly the different methodologies of those services should be expounded comparativly on that occasion in order to represent advantages and disadvantages. The use of ontologies and folksonomies as implementations in recommender systems is portrayed in order to be able to take into consideration chances and risks of the application of knowledge representation methods for future researches.ws07Community for tag(s) ws07