@inproceedings{conf/sww/MiddletonASR02, title = {Exploiting Synergy Between Ontologies and Recommender Systems.}, author = {Stuart E. Middleton and Harith Alani and Nigel Shadbolt and David De Roure}, booktitle = {Semantic Web Workshop}, crossref = {conf/sww/2002}, editor = {Martin Frank and Natasha F. Noy and Steffen Staab}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, volume = 55, year = 2002, url = {http://dblp.uni-trier.de/db/conf/sww/sww2002.html#MiddletonASR02}, ee = {http://SunSITE.Informatik.RWTH-Aachen.DE/Publications/CEUR-WS/Vol-55/middleton.pdf}, date = {2003-04-02}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2816daaef7845122e39b0fbaba9a4ee79/ans}, keywords = {synergy projekt ontology seminar recommender knowledge ws07 kde} } @inproceedings{500755, title = {Capturing knowledge of user preferences: ontologies in recommender systems}, address = {New York, NY, USA}, author = {Stuart E. Middleton and David C. De Roure and Nigel R. Shadbolt}, booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture}, pages = {100--107}, publisher = {ACM Press}, year = 2001, url = {http://portal.acm.org/citation.cfm?id=500755}, location = {Victoria, British Columbia, Canada}, isbn = {1-58113-380-4}, doi = {http://doi.acm.org/10.1145/500737.500755}, description = {Capturing knowledge of user preferences}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/26d0a7792db2c0f96bd0a495a56e57464/ans}, keywords = {ws07 user knowledge recommender kde ontologies seminar projekt} } @inbook{LawAlmKotVivDur, title = {Personalization of Supermarket Product Recommendations}, author = {R.D. Lawrence and G.S. Almasi and V. Kotlyar and M.S. Viveros and S.S. Duri}, booktitle = {Applications of Data Mining to Electronic Commerce}, editor = {Ronny Kohavi and Foster Provost}, number = {1/2}, pages = {11-32}, publisher = {Kluwer Academic Publishers}, volume = 5, year = 2001, biburl = {http://www.bibsonomy.org/bibtex/22554424935c30f797f4854dc11a19c33/ans}, keywords = {seminar kde rules projekt ws07 clustering recommender} } @inproceedings{Towle00, title = {Knowledge Based Recommender Systems Using Explicit User Models}, author = {B. Towle and C. Quinn}, booktitle = {Papers from the AAAI Workshop, AAAI Technical Report WS-00-04}, pages = {74-77}, publisher = {Menlo Park, CA: AAAI Press}, year = 2000, description = {WSD}, biburl = {http://www.bibsonomy.org/bibtex/2621c69ecfcd0680c553282dd1d29225a/ans}, keywords = {knowledge user ws07 kde recommender seminar projekt} } @article{burke:2000, title = {Knowledge--Based Recommender Systems}, author = {R. Burke}, journal = {Encyclopedia of Library and Information Science}, number = 32, organization = {Marcel Dekker, Inc.}, volume = 69, year = 2000, description = {all-bibs-cleaned.bib}, biburl = {http://www.bibsonomy.org/bibtex/27c966790c8d4a8be142db2a47e80f9d7/ans}, keywords = {ws07 knowledge recommender projekt seminar kde} } @inproceedings{Byde2007, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, author = {Andrew Byde and Hui Wan and Steve Cayzer}, booktitle = {Proceedings of the International Conference on Weblogs and Social Media}, month = {March}, year = 2007, url = {http://www.icwsm.org/papers/paper47.html}, priority = {5}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2157846898c1c2a65c265a913ebac115a/ans}, keywords = {seminar content ws07 recommender kde bookmarking projekt tagging classification} } @inproceedings{Fuetel-SurfLen, title = {{Mining Navigation History for Recommendation.}}, address = {New York, NY, USA}, author = {Xiaobin Fu and Jay Budzik and Kristian J. Hammond}, booktitle = {IUI '00: Proceedings of the 5th International Conference on Intelligent User Interfaces}, pages = {106--112}, publisher = {ACM Press}, year = 2000, location = {New Orleans, Louisiana, United States}, isbn = {1-58113-134-8}, doi = {http://doi.acm.org/10.1145/325737.325796}, description = {all-bibs-cleaned.bib}, biburl = {http://www.bibsonomy.org/bibtex/2909a5f2d78fa425920ed29c346a4e60a/ans}, keywords = {rules recommender learning history navigation ws07 projekt kde seminar} } @misc{batagelj-2002, title = {Generalized Cores}, author = {V. Batagelj and M. Zaversnik}, note = {cs.DS/0202039}, year = 2002, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0202039}, description = {[cs/0202039] Generalized Cores}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/204dd5c8a505463b1e196f842b91a8b07/ans}, keywords = {projekt graph seminar core recommender generalized kcore analysis kde network ws07} } @article{keyhere, title = {kNN Versus SVM in the Collaborative Filtering Framework}, author = {Miha Grčar and Blaž Fortuna and Dunja Mladenič and Marko Grobelnik}, journal = {Data Science and Classification}, pages = {251--260}, year = 2006, url = {http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf}, doi = {http://dx.doi.org/10.1007/3-540-34416-0_27}, description = {SpringerLink - Book Chapter}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/249c80c0aeb3c7eeb1941dcb62a5d26f3/ans}, keywords = {knn seminar kde projekt svm classification recommender ws07 learning} } @inproceedings{658298, title = {Collaborative Learning and Recommender Systems}, address = {San Francisco, CA, USA}, author = {Wee Sun Lee}, booktitle = {ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning}, pages = {314--321}, publisher = {Morgan Kaufmann Publishers Inc.}, year = 2001, url = {http://www.comp.nus.edu.sg/~leews/publications/icml01.pdf}, isbn = {1-55860-778-1}, description = {Collaborative Learning and Recommender Systems}, biburl = {http://www.bibsonomy.org/bibtex/2cf5ddf4740a73d8c161e704cac3240f6/ans}, keywords = {kde learning seminar recommender ws07 projekt classification} } @inproceedings{657311, title = {Learning Collaborative Information Filters}, address = {San Francisco, CA, USA}, author = {Daniel Billsus and Michael J. Pazzani}, booktitle = {ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning}, pages = {46--54}, publisher = {Morgan Kaufmann Publishers Inc.}, year = 1998, url = {http://www.ics.uci.edu/~pazzani/Publications/MLC98.pdf}, isbn = {1-55860-556-8}, description = {Learning Collaborative Information Filters}, biburl = {http://www.bibsonomy.org/bibtex/2977851e8e6cb73b8b94b0cea69dbb9e3/ans}, keywords = {classification learning ws07 seminar projekt recommender kde} } @inproceedings{295795, title = {Recommendation as classification: using social and content-based information in recommendation}, address = {Menlo Park, CA, USA}, author = {Chumki Basu and Haym Hirsh and William Cohen}, booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence}, pages = {714--720}, publisher = {American Association for Artificial Intelligence}, year = 1998, url = {ftp://ftp.cs.rutgers.edu/pub/hirsh/papers/1998/aaai1.ps}, location = {Madison, Wisconsin, United States}, isbn = {0-262-51098-7}, biburl = {http://www.bibsonomy.org/bibtex/290f4b7eab8a7a308c6e077a993cd19d8/ans}, keywords = {learning kde classification projekt seminar recommender ws07} } @article{Adomavicius:2005, title = {Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions}, author = {G. Adomavicius and A. Tuzhilin}, booktitle = {Knowledge and Data Engineering, IEEE Transactions on}, pages = {734- 749}, volume = 17, year = 2005, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1423975}, issn = {1041-4347}, doi = {10.1109/TKDE.2005.99}, description = {Welcome to IEEE Xplore 2.0: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2d67034c865879740160f687448cacaa3/ans}, keywords = {recommender ws07 survey extension projekt seminar kde} } @inproceedings{hotho06information, title = {Information Retrieval in Folksonomies: Search and Ranking}, address = {Heidelberg}, author = {Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme}, booktitle = {The Semantic Web: Research and Applications}, editor = {York Sure and John Domingue}, pages = {411-426}, publisher = {Springer}, series = {LNAI}, volume = 4011, year = 2006, url = {http://.kde.cs.uni-kassel.de/hotho}, lastdatemodified = {2006-07-18}, pdf = {hotho06-information.pdf}, read = {read}, lastname = {Hotho}, own = {own}, description = {Information Retrieval in Folksonomies: Search and Ranking}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/23c301945817681d637ee43901c016939/ans}, keywords = {projekt recommender ir folksonomy tagging seminar kde folkrank ws07} } @inproceedings{DBLP:conf/pkdd/JaschkeMHSS07, title = {Tag Recommendations in Folksonomies}, author = {Robert Jäschke and Leandro Balby Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings}, crossref = {DBLP:conf/pkdd/2007}, editor = {Joost N. Kok and Jacek Koronacki and Ramon López de Mántaras and Stan Matwin and Dunja Mladenic and Andrzej Skowron}, pages = {506-514}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 4702, year = 2007, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf}, ee = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {978-3-540-74975-2}, biburl = {http://www.bibsonomy.org/bibtex/21df0274ddea4223119f1090f236a6f1f/ans}, keywords = {pagerank seminar ws07 projekt tagging recommender kde folksonomy} } @inproceedings{thieme:recommender, title = {Compound Classification Models for Recommender Systems.}, address = {Houston, Texas, USA}, author = {Lars Schmidt-Thieme}, booktitle = {Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005}, pages = {378-385}, publisher = {IEEE Computer Society}, year = 2005, url = {http://www.informatik.uni-freiburg.de/cgnm/pub/pdfs/Schmidt-Thieme2005-compound-classifiers-for-recommender-systems.pdf}, ee = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.46}, bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {0-7695-2278-5}, biburl = {http://www.bibsonomy.org/bibtex/22c3aea62cafdd8a01a68e3fbed7b4071/ans}, keywords = {seminar recommender ws07 kde projekt classification learning} } @article{ieKey, title = {Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art}, author = {Stefanie Höhfeld and Melanie Kwiatkowski}, journal = {IWP-Information Wissenschaft & Praxis}, number = 5, pages = {265-276}, volume = 58, year = 2007, url = {http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/58/1189509550empfehlung.pdf}, description = {Recommendersysteme - Informationswissenschaft}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2501849ade58a25831e72519f6add1313/ans}, keywords = {kde folksonomy recommender projekt seminar survey ws07} }