BibSonomy publications for /user/wnpxrz/filteringThu Jan 24 17:54:07 CET 2008Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)The Dynamics and Semantics of Collaborative Tagging 2006collaborative filtering tagging 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.Thu Jan 24 17:47:05 CET 2008New York, NY, USAACM Trans. Inf. Syst.15--53Evaluating collaborative filtering recommender systems222004collaborative evaluation filtering recommendersystems 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.Fri Jan 18 16:31:17 CET 2008Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering2004collaborative filtering imported Fri Jan 18 15:51:58 CET 2008Content-boosted collaborative filtering2001collaborative filtering imported Fri Jan 18 15:46:27 CET 2008Clustering items for collaborative filtering2001collaborative filtering imported 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 use1995collaborative filtering imported Wed Jan 09 21:16:16 CET 2008New York, NY, USACommun. ACM1261--70Using collaborative filtering to weave an information tapestry351992collaborative filtering imported Wed Jan 09 21:15:39 CET 2008New York, NY, USACommun. ACM377--87GroupLens: applying collaborative filtering to Usenet news401997collaborative filtering grouplens imported Thu Jan 03 17:33:41 CET 2008Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis2003collaborative filtering gaussian imported lsa Sun Dec 30 11:50:57 CET 2007A spreading activation approach for collaborative filtering2007collaborative filtering imported 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.Sun Dec 30 11:43:14 CET 2007Beyond Recommender Systems: Helping People Help Each Other2001collaborative filtering imported recommendersystems Wed Nov 21 16:12:16 CET 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandAutomated Tag Clustering: Improving search and exploration in the tag space2006clustering collaborative exploration filtering folksonomy search tag tags 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 Papers18362000collaborative filtering profile profiling proj:bk proj:mh user web 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 recommendation1998classification collaborative content filtering recommendersystems social Wed Nov 21 16:00:50 CET 2007San Francisco, CA, USAICML '98: Proceedings of the Fifteenth International Conference on Machine Learning46--54Learning Collaborative Information Filters1998collaborative filtering machinelearning 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 Submissions2006collaborative filtering folksonomy imported recommendersystems tagging tags web 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.Mon Nov 19 15:48:13 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006bookmarking collaborative filtering recommendersystems social tagging 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 -Mon Nov 19 15:46:22 CET 2007Proceedings of the 14$^{th}$ Conference on Uncertainty in Artificial Intelligence43-52Empirical Analysis of Predictive Algorithms for Collaborative Filtering1998collaborative evaluation filtering Tue Nov 13 16:05:57 CET 2007A Flexible Mixture Model for Collaborative Filtering2003collaborative filtering imported mixture model Tue Nov 13 16:05:37 CET 2007An Automatic Weighting Scheme for Collaborative Filtering2004collaborative filtering imported weighting