BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
10,"","batagelj-2002","Batagelj, V. & Zaversnik, M.","Generalized Cores","",,,"","",2002,"","cs.DS/0202039","http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0202039","","","","","","","","","","","","","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.","","analysis core generalized graph kcore kde network projekt recommender seminar ws07 ","",""
7,"","burke:2000","Burke, R.","Knowledge--Based Recommender Systems","Encyclopedia of Library and Information Science",69,32,"","",2000,"","","","","","","","","","","","","Marcel Dekker, Inc.","","","","","kde knowledge projekt recommender seminar ws07 ","",""
6,"","Byde2007","Byde, Andrew; Wan, Hui & Cayzer, Steve","Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics","",,,"March","",2007,"","","http://www.icwsm.org/papers/paper47.html","Proceedings of the International Conference on Weblogs and Social Media","","","","","","","","","","","","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.","","bookmarking classification content kde projekt recommender seminar tagging ws07 ","",""
6,"1-58113-134-8","Fuetel-SurfLen","Fu, Xiaobin; Budzik, Jay & Hammond, Kristian J.","Mining Navigation History for Recommendation.","",,,"","106--112",2000,"New York, NY, USA","","","IUI '00: Proceedings of the 5th International Conference on Intelligent User Interfaces","","","","","ACM Press","","","","","","","","","history kde learning navigation projekt recommender rules seminar ws07 ","",""
7,"","keyhere","Grčar, Miha; Fortuna, Blaž; Mladenič, Dunja & Grobelnik, Marko","kNN Versus SVM in the Collaborative Filtering Framework","Data Science and Classification",,,"","251--260",2006,"","","http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf","","","","","","","","","","","","","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.","","classification kde knn learning projekt recommender seminar svm ws07 ","",""
5,"","LawAlmKotVivDur","Lawrence, R.D.; Almasi, G.S.; Kotlyar, V.; Viveros, M.S. & Duri, S.S.","Personalization of Supermarket Product Recommendations","",5,1/2,"","11-32",2001,"","","","Applications of Data Mining to Electronic Commerce","","","","Kohavi, Ronny & Provost, Foster","Kluwer Academic Publishers","","","","","","","","","clustering kde projekt recommender rules seminar ws07 ","",""
6,"1-55860-778-1","658298","Lee, Wee Sun","Collaborative Learning and Recommender Systems","",,,"","314--321",2001,"San Francisco, CA, USA","","http://www.comp.nus.edu.sg/~leews/publications/icml01.pdf","ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning","","","","","Morgan Kaufmann Publishers Inc.","","","","","","","","","classification kde learning projekt recommender seminar ws07 ","",""
6,"","conf/sww/MiddletonASR02","Middleton, Stuart E.; Alani, Harith; Shadbolt, Nigel & Roure, David De","Exploiting Synergy Between Ontologies and Recommender Systems.","",55,,"","",2002,"","","http://dblp.uni-trier.de/db/conf/sww/sww2002.html#MiddletonASR02","Semantic Web Workshop","","","CEUR Workshop Proceedings","Frank, Martin; Noy, Natasha F. & Staab, Steffen","CEUR-WS.org","","","","","","","","","kde knowledge ontology projekt recommender seminar synergy ws07 ","",""
6,"1-58113-380-4","500755","Middleton, Stuart E.; Roure, David C. De & Shadbolt, Nigel R.","Capturing knowledge of user preferences: ontologies in recommender systems","",,,"","100--107",2001,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=500755","K-CAP '01: Proceedings of the 1st international conference on Knowledge capture","","","","","ACM Press","","","","","","","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.","","kde knowledge ontologies projekt recommender seminar user ws07 ","",""
6,"","Towle00","Towle, B. & Quinn, C.","Knowledge Based Recommender Systems Using Explicit User Models","",,,"","74-77",2000,"","","","Papers from the AAAI Workshop, AAAI Technical Report WS-00-04","","","","","Menlo Park, CA: AAAI Press","","","","","","","","","kde knowledge projekt recommender seminar user ws07 ","",""