@inproceedings{karypis2001evaluation, abstract = {The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.}, acmid = {502627}, address = {New York, NY, USA}, author = {Karypis, George}, booktitle = {Proceedings of the Tenth International Conference on Information and Knowledge Management}, doi = {10.1145/502585.502627}, interhash = {ad804add9a1dec7cb4df3c98fac7dc13}, intrahash = {234c68832d68a4530e3ba8e2fb533043}, isbn = {1-58113-436-3}, location = {Atlanta, Georgia, USA}, numpages = {8}, pages = {247--254}, publisher = {ACM}, series = {CIKM '01}, title = {Evaluation of Item-Based Top-N Recommendation Algorithms}, url = {http://doi.acm.org/10.1145/502585.502627}, year = 2001 } @incollection{leake2000casebased, abstract = {Case-based reasoning(CBR) is an artificial intelligence paradigm for reasoning and learning. Case-based reasoning solves new problems by retrieving stored records of prior problem-solving episodes (cases) and adapting their solutions to fit new circumstances. Each processing episode provides a new case that is stored for future reuse, making learning a natural side-effect of the reasoning process. Case-based reasoning is also studied within cognitive science as a model of human reasoning: studies show that people use recollections of prior problems to guide their reasoning in a wide range of tasks, such as programming, mathematical problem solving, diagnosis, decision making, and design.}, acmid = {1074199}, address = {Chichester, UK}, author = {Leake, David B.}, booktitle = {Encyclopedia of Computer Science}, edition = {4th}, editor = {Ralston, Anthony and Reilly, Edwin D. and Hemmendinger, David}, interhash = {fa414e2f48be14bb94cbfbf2566e36af}, intrahash = {b8526b7c03f1fc9bdd85863dfbf881a2}, isbn = {0-470-86412-5}, month = jun, numpages = {2}, pages = {196--197}, publisher = {John Wiley and Sons Ltd.}, title = {Case-based reasoning}, url = {http://dl.acm.org/citation.cfm?id=1074100.1074199}, year = 2000 } @article{Jandt2008b, author = {Jandt, Silke}, editor = {Jandt, Silke}, interhash = {b16eeb708f6712a71e72f9f433000593}, intrahash = {4fa787c862646417a4c989e2b0a49978}, title = {Vertrauen im Mobile Commerce – Vorschläge für die rechtsverträgliche Gestaltung von Location Based Services}, year = 2008 } @article{Jandt2008, author = {Schnabel, Christoph and Jandt, Silke}, editor = {Jandt, Christoph Schnabel Silke}, interhash = {6d2c22e5c749ca4f72c8315e874d9dff}, intrahash = {baf66c4e19b6e6a2fd60ff1dc185cefb}, pages = {723-729}, title = {Location Based Services im Fokus des Datenschutzes}, year = 2008 } @article{jrg1998densitybased, abstract = {The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm—called GDBSCAN—can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems. ER -}, author = {Sander, Jörg and Ester, Martin and Kriegel, Hans-Peter and Xu, Xiaowei}, interhash = {3f2615cbf7c60d63f0a1ccc82e0caea1}, intrahash = {a15f4445f49f37f272b373c69231a590}, journal = {Data Mining and Knowledge Discovery}, month = {#jun#}, number = 2, pages = {169--194}, title = {Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications}, url = {http://dx.doi.org/10.1023/A:1009745219419}, volume = 2, year = 1998 } @inproceedings{Ester1996, author = {Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei}, booktitle = {Proc. of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96)}, file = {:KDD96-037.pdf:PDF}, interhash = {ba33e4d6b4e5b26bd9f543f26b7d250a}, intrahash = {2f9e50f0a003c4d3067cab2b6fa47fe0}, pages = {226-231}, title = {A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise}, year = 1996 } @inproceedings{FalBarSpi07, author = {Falkowski, Tanja and Barth, Anja and Spiliopoulou, Myra}, booktitle = {In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,}, interhash = {abd9653fc405547fd263c72c5bc5ae88}, intrahash = {c0f9b82222d0c9a0b1cb0a5fa41a735a}, pages = {112-115}, title = {DENGRAPH: A Density-based Community Detection Algorithm}, url = {http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf}, year = 2007 } @inproceedings{conf/das/SchenkerBLK04, author = {Schenker, Adam and Bunke, Horst and Last, Mark and Kandel, Abraham}, booktitle = {Document Analysis Systems}, crossref = {conf/das/2004}, date = {2005-01-05}, editor = {Marinai, Simone and Dengel, Andreas}, ee = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3163&spage=401}, interhash = {83ba06e8918a227fb2345e047e40f619}, intrahash = {4450261ce5af13db99ce208800dff22c}, isbn = {3-540-23060-2}, pages = {401-412}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Graph-Based Framework for Web Document Mining.}, url = {http://dblp.uni-trier.de/db/conf/das/das2004.html#SchenkerBLK04}, volume = 3163, year = 2004 } @inproceedings{lang95newsweeder, author = {Lang, Ken}, booktitle = {Proceedings of the 12th International Conference on Machine Learning}, interhash = {e64ed50bf2d9ceb44e38ec59c0947207}, intrahash = {b738abb5a0f2cae47e8f0633460c69a3}, pages = {331--339}, publisher = {Morgan Kaufmann publishers Inc.: San Mateo, CA, USA}, title = {News{W}eeder: learning to filter netnews}, url = {http://citeseer.ist.psu.edu/lang95newsweeder.html}, year = 1995 } @inproceedings{halevymadhavan2003, author = {Halevy, Alon Y. and Madhavan, Jayant}, bibsource = {DBLP, http://dblp.uni-trier.de}, booktitle = {IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, August 9-15, 2003}, editor = {Gottlob, Georg and Walsh, Toby}, interhash = {296e995087df1b2d67f0ceb22510aa58}, intrahash = {75d3c0fdd9fdbbfebd93cfda1ee42d28}, pages = {1567-1572}, publisher = {Morgan Kaufmann}, title = {Corpus-Based Knowledge Representation}, year = 2003 } @inproceedings{leeicdm2001, author = {Lee, Jung-Won and Lee, Kiho and Kim, Won}, booktitle = {Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November - 2 December 2001, San Jose, California, USA}, editor = {Cercone, Nick and Lin, Tsau Young and Wu, Xindong}, interhash = {23dd8c004ec7cd9a380101edcfc7c31b}, intrahash = {e8fc4f311c34c1007c794379c1629d73}, location = {Madrid}, pages = {345-352}, publisher = {IEEE Computer Society}, title = {Preparations for Semantics-Based XML Mining}, year = 2001 } @article{KLW90, author = {Kifer, M. and Lausen, G. and Wu, J.}, interhash = {a14ad8e99a051f8a341b1e2a86c09713}, intrahash = {7068223da6c726e7db1c7017f42ccfc5}, isbn = {3-540-41066-X}, journal = {Journal of the ACM}, number = 4, pages = {741--843}, title = {Logical Foundations of Object-Oriented and Frame-Based Languages}, volume = 42, year = 1995 } @inproceedings{MobasheretalCHI2001, address = {New Orleans, LA}, author = {Parent, S. and Mobasher, B. and Lytinen, S.}, booktitle = {Proceedings of the 9th International Conference on Human Computer Interaction}, interhash = {9539ed5408f62ddb8d5c790fabac2e76}, intrahash = {aaf8f9dccc3d89097704896af6b78536}, title = {An Adaptive Agent for Web Exploration Based of Concept Hierarchies}, url = {citeseer.nj.nec.com/443168.html}, url2 = {www.cs.umn.edu/research/websift/papers/rwc\_thesis.ps}, year = 2001 } @article{MobasheretalCACM, author = {Mobasher, B. and Cooley, R. and Srivastava, J.}, interhash = {98d5090dafb39596483c75dc4a6846c3}, intrahash = {a7a6cdb6e0790b276d7f0642991e734e}, journal = {Communications of the ACM}, location = {Santa Barbara, CA}, number = 8, pages = {142--151}, title = {Automatic personalization based on Web usage mining}, volume = 43, year = 2000 }