http://www.bibsonomy.org/burst/user/wnpxrz/collaborativeBibSonomy BuRST Feed for /user/wnpxrz/collaborative2008-07-26T21:43:45+02:00introduces different categories of tags (eg task-organising, categories, etc)http://www.bibsonomy.org/bibtex/2281943ad0a7c81ad975f0a4c7de43447/wnpxrzwnpxrz2008-04-03T16:31:54+02:00tagging collaborative <span style="color:#555555;">S.A. <a href="http://www.bibsonomy.org/author/Golder">Golder</a> and B.A. <a href="http://www.bibsonomy.org/author/Huberman">Huberman</a> </span><em>Journal of Information Science</em><em>32(2):198</em>(<em>2006</em>)Thu Apr 03 16:31:54 CEST 2008Journal of Information Science2198{Usage patterns of collaborative tagging systems}322006tagging collaborative http://www.bibsonomy.org/bibtex/27be112719b93a4d4263407afbf05cce1/wnpxrzwnpxrz2008-01-24T17:54:07+01:00tagging filtering collaborative <span style="color:#555555;">Harry <a href="http://www.bibsonomy.org/author/Halpin">Halpin</a> and Valentin <a href="http://www.bibsonomy.org/author/Robu">Robu</a> and Hana <a href="http://www.bibsonomy.org/author/Shepard">Shepard</a> </span><em>Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06), </em>(<em>2006</em>)Thu Jan 24 17:54:07 CET 2008Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)The Dynamics and Semantics of Collaborative Tagging 2006tagging filtering collaborative The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classi- fication schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from “popular” tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.Evaluating collaborative filtering recommender systemshttp://www.bibsonomy.org/bibtex/2bdd3980bb3c297d1b84ceb0c7729d397/wnpxrzwnpxrz2008-01-24T17:47:05+01:00recommendersystems collaborative evaluation filtering <span style="color:#555555;">Jonathan L. <a href="http://www.bibsonomy.org/author/Herlocker">Herlocker</a> and Joseph A. <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> and Loren G. <a href="http://www.bibsonomy.org/author/Terveen">Terveen</a> and John T. <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>ACM Trans. Inf. Syst.</em><em>22(1):5--53</em>(<em>2004</em>)Thu Jan 24 17:47:05 CET 2008New York, NY, USAACM Trans. Inf. Syst.15--53Evaluating collaborative filtering recommender systems222004recommendersystems collaborative evaluation filtering Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.DLIST - Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filteringhttp://www.bibsonomy.org/bibtex/2d32c3bfdeb71734dd020d29f9ccae7d6/wnpxrzwnpxrz2008-01-18T16:31:17+01:00collaborative filtering imported <span style="color:#555555;">Zan <a href="http://www.bibsonomy.org/author/Huang">Huang</a> and Hsinchun <a href="http://www.bibsonomy.org/author/Chen">Chen</a> and Daniel <a href="http://www.bibsonomy.org/author/Zeng">Zeng</a> </span>(<em>2004</em>)Fri Jan 18 16:31:17 CET 2008Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering2004collaborative filtering imported Content-Boosted Collaborative Filtering - Melville, Mooney, Nagarajan (ResearchIndex)http://www.bibsonomy.org/bibtex/275a1b0eda56eb490f1ae43f9887e1605/wnpxrzwnpxrz2008-01-18T15:51:58+01:00filtering imported collaborative <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Melville">Melville</a> and R. <a href="http://www.bibsonomy.org/author/Mooney">Mooney</a> and R. <a href="http://www.bibsonomy.org/author/Nagarajan">Nagarajan</a> </span>(<em>2001</em>)Fri Jan 18 15:51:58 CET 2008Content-boosted collaborative filtering2001filtering imported collaborative Clustering Items for Collaborative Filtering - Connor, Herlocker (ResearchIndex)http://www.bibsonomy.org/bibtex/2c0928a69f114f03cc84de845b044c39d/wnpxrzwnpxrz2008-01-18T15:46:27+01:00collaborative imported filtering <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Connor">Connor</a> and J. <a href="http://www.bibsonomy.org/author/Herlocker">Herlocker</a> </span>(<em>2001</em>)Fri Jan 18 15:46:27 CET 2008Clustering items for collaborative filtering2001collaborative imported filtering Recommending and evaluating choices in a virtual community of usehttp://www.bibsonomy.org/bibtex/2e111f8272ac836796cd546765cd86d0f/wnpxrzwnpxrz2008-01-09T21:17:07+01:00filtering collaborative imported <span style="color:#555555;">Will <a href="http://www.bibsonomy.org/author/Hill">Hill</a> and Larry <a href="http://www.bibsonomy.org/author/Stead">Stead</a> and Mark <a href="http://www.bibsonomy.org/author/Rosenstein">Rosenstein</a> and George <a href="http://www.bibsonomy.org/author/Furnas">Furnas</a> </span><em>CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems, </em><em>page194--201. </em><em>New York, NY, USA, </em><em>ACM Press/Addison-Wesley Publishing Co., </em>(<em>1995</em>)Wed Jan 09 21:17:07 CET 2008New York, NY, USACHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems194--201Recommending and evaluating choices in a virtual community of use1995filtering collaborative imported Using collaborative filtering to weave an information tapestryhttp://www.bibsonomy.org/bibtex/2f4c5048e4b774037f64ca88ad0bcd8b5/wnpxrzwnpxrz2008-01-09T21:16:16+01:00imported filtering collaborative <span style="color:#555555;">David <a href="http://www.bibsonomy.org/author/Goldberg">Goldberg</a> and David <a href="http://www.bibsonomy.org/author/Nichols">Nichols</a> and Brian M. <a href="http://www.bibsonomy.org/author/Oki">Oki</a> and Douglas <a href="http://www.bibsonomy.org/author/Terry">Terry</a> </span><em>Commun. ACM</em><em>35(12):61--70</em>(<em>1992</em>)Wed Jan 09 21:16:16 CET 2008New York, NY, USACommun. ACM1261--70Using collaborative filtering to weave an information tapestry351992imported filtering collaborative GroupLenshttp://www.bibsonomy.org/bibtex/2b53b2dfa6b9064d2ad77fd6440ca48b3/wnpxrzwnpxrz2008-01-09T21:15:39+01:00filtering grouplens imported collaborative <span style="color:#555555;">Joseph A. <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> and Bradley N. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> and David <a href="http://www.bibsonomy.org/author/Maltz">Maltz</a> and Jonathan L. <a href="http://www.bibsonomy.org/author/Herlocker">Herlocker</a> and Lee R. <a href="http://www.bibsonomy.org/author/Gordon">Gordon</a> and John <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>Commun. ACM</em><em>40(3):77--87</em>(<em>1997</em>)Wed Jan 09 21:15:39 CET 2008New York, NY, USACommun. ACM377--87GroupLens: applying collaborative filtering to Usenet news401997filtering grouplens imported collaborative Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis (ResearchIndex)http://www.bibsonomy.org/bibtex/2ee9a97fe4b415a84b145ca5b752a8c46/wnpxrzwnpxrz2008-01-03T17:33:41+01:00imported filtering lsa collaborative gaussian <span style="color:#555555;">Thomas <a href="http://www.bibsonomy.org/author/Hofmann">Hofmann</a> </span>(<em>2003</em>)Thu Jan 03 17:33:41 CET 2008Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis2003imported filtering lsa collaborative gaussian [0712.3807] A spreading activation approach for collaborative filteringhttp://www.bibsonomy.org/bibtex/2151260b54a8e92cbc4c940745ee6bdd8/wnpxrzwnpxrz2007-12-30T11:50:57+01:00imported collaborative filtering <span style="color:#555555;">Jian-Guo <a href="http://www.bibsonomy.org/author/Liu">Liu</a> </span>(<em>2007</em>)Sun Dec 30 11:50:57 CET 2007A spreading activation approach for collaborative filtering2007imported collaborative filtering In this Brief Report, we propose a spreading activation approach for collaborative filtering (SA-CF). Under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthermore, we introduce a free parameter $\beta$ to regulate the contributions of objects to user-user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-$N$ similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.Beyond Recommender Systems: Helping People Help Each Other - Terveen, Hill (ResearchIndex)http://www.bibsonomy.org/bibtex/2e26b0d4305079fae673c8dcdb1999d68/wnpxrzwnpxrz2007-12-30T11:43:14+01:00filtering collaborative recommendersystems imported <span style="color:#555555;">L. <a href="http://www.bibsonomy.org/author/Terveen">Terveen</a> and W. <a href="http://www.bibsonomy.org/author/Hill">Hill</a> </span>(<em>2001</em>)Sun Dec 30 11:43:14 CET 2007Beyond Recommender Systems: Helping People Help Each Other2001filtering collaborative recommendersystems imported Vocabulary growth in collaborative tagging systemshttp://www.bibsonomy.org/bibtex/24d6bfbd16ba976f4a4aae985c2a92449/wnpxrzwnpxrz2007-12-04T16:09:52+01:00tagging collaborative <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> and Andrea <a href="http://www.bibsonomy.org/author/Baldassarri">Baldassarri</a> and Vito D. P. <a href="http://www.bibsonomy.org/author/Servedio">Servedio</a> and Vittorio <a href="http://www.bibsonomy.org/author/Loreto">Loreto</a> </span>(<em>2007</em>)Tue Dec 04 16:09:52 CET 2007Vocabulary growth in collaborative tagging systems2007tagging collaborative http://www.bibsonomy.org/bibtex/22571baaa4ea731965489cb66e6288886/wnpxrzwnpxrz2007-11-30T20:30:34+01:00hierarchical collaborative bookmark classification <span style="color:#555555;">Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> and Karen H. L. <a href="http://www.bibsonomy.org/author/Tso">Tso</a> and Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> </span><em>Special Issue of the Computer Networks journal on Innovations in Web Communications Infrastructure</em>(<em>2007</em>)Fri Nov 30 20:30:34 CET 2007Special Issue of the Computer Networks journal on Innovations in Web Communications InfrastructureSupporting Collaborative Hierarchical Classification: Bookmarks as an Example2007hierarchical collaborative bookmark classification http://www.bibsonomy.org/bibtex/276b741061fab004645c3119db5a17bc3/wnpxrzwnpxrz2007-11-21T16:12:16+01:00exploration folksonomy search tag filtering clustering tags collaborative <span style="color:#555555;">Grigory <a href="http://www.bibsonomy.org/author/Begelman">Begelman</a> and Philipp <a href="http://www.bibsonomy.org/author/Keller">Keller</a> and Frank <a href="http://www.bibsonomy.org/author/Smadja">Smadja</a> </span><em>Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, </em>(<em>2006</em>)Wed Nov 21 16:12:16 CET 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandAutomated Tag Clustering: Improving search and exploration in the tag space2006exploration folksonomy search tag filtering clustering tags collaborative http://www.bibsonomy.org/bibtex/218a9697e8ca04f637487e79b6be9cc83/wnpxrzwnpxrz2007-11-21T16:11:24+01:00proj:bk web filtering user profiling proj:mh collaborative profile <span style="color:#555555;">Brij M. <a href="http://www.bibsonomy.org/author/Masand">Masand</a> and Myra <a href="http://www.bibsonomy.org/author/Spiliopoulou">Spiliopoulou</a> (eds.). </span><em>volume1836ofLecture Notes in Computer Science, </em><em>Springer, </em>(<em>2000</em>)Wed Nov 21 16:11:24 CET 2007WEBKDDLecture Notes in Computer ScienceWeb Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers18362000proj:bk web filtering user profiling proj:mh collaborative profile http://www.bibsonomy.org/bibtex/290f4b7eab8a7a308c6e077a993cd19d8/wnpxrzwnpxrz2007-11-21T16:02:57+01:00recommendersystems collaborative social classification filtering content <span style="color:#555555;">Chumki <a href="http://www.bibsonomy.org/author/Basu">Basu</a> and Haym <a href="http://www.bibsonomy.org/author/Hirsh">Hirsh</a> and William <a href="http://www.bibsonomy.org/author/Cohen">Cohen</a> </span><em>AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, </em><em>page714--720. </em><em>Menlo Park, CA, USA, </em><em>American Association for Artificial Intelligence, </em>(<em>1998</em>)Wed Nov 21 16:02:57 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 recommendation1998recommendersystems collaborative social classification filtering content Learning Collaborative Information Filtershttp://www.bibsonomy.org/bibtex/2977851e8e6cb73b8b94b0cea69dbb9e3/wnpxrzwnpxrz2007-11-21T16:00:50+01:00filtering machinelearning collaborative <span style="color:#555555;">Daniel <a href="http://www.bibsonomy.org/author/Billsus">Billsus</a> and Michael J. <a href="http://www.bibsonomy.org/author/Pazzani">Pazzani</a> </span><em>ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning, </em><em>page46--54. </em><em>San Francisco, CA, USA, </em><em>Morgan Kaufmann Publishers Inc., </em>(<em>1998</em>)Wed Nov 21 16:00:50 CET 2007San Francisco, CA, USAICML '98: Proceedings of the Fifteenth International Conference on Machine Learning46--54Learning Collaborative Information Filters1998filtering machinelearning collaborative Welcome to IEEE Xplore 2.0: Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissionshttp://www.bibsonomy.org/bibtex/2b1cb4183d3ad183709ed11780f1b5fdf/wnpxrzwnpxrz2007-11-19T15:49:43+01:00web tagging recommendersystems filtering tags imported collaborative folksonomy <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Niwa">Niwa</a> and Takuo <a href="http://www.bibsonomy.org/author/Doi">Doi</a> and S. <a href="http://www.bibsonomy.org/author/Honiden">Honiden</a> </span><em>Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on, </em><em>page388- 393. </em>(<em>2006</em>)Mon Nov 19 15:49:43 CET 2007Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on388- 393Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions2006web tagging recommendersystems filtering tags imported collaborative folksonomy There have been many attempts to construct web page recommender systems using collaborative filtering. But the domains these systems can cover are very restricted because it is very difficult to assemble user preference data to web pages, and the number of web pages on the Internet is too large. In this paper, we propose the way to construct a new type of web page recommender system covering all over the Internet, by using Folksonomy and Social Bookmark which are getting very popular in these days.SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/wnpxrzwnpxrz2007-11-19T15:48:13+01:00bookmarking tagging collaborative recommendersystems filtering social <span style="color:#555555;">Yanfei <a href="http://www.bibsonomy.org/author/Xu">Xu</a> and Liang <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Wei <a href="http://www.bibsonomy.org/author/Liu">Liu</a> </span><em>Frontiers of WWW Research and Development - APWeb 2006</em>(<em>2006</em>)Mon Nov 19 15:48:13 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006bookmarking tagging collaborative recommendersystems filtering social Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -