BibSonomy publications for /user/hotho/datasetSat Apr 25 10:09:02 CEST 2009Proceedings from the 2nd International Conference on Weblogs and Social Media (AAAI)A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks2008analysis dataset myspace networking social Fri Jan 16 12:08:23 CET 2009InScit2006: International Conference on Multidisciplinary Information Sciences and TechnologiesImproving Tag-Clouds as Visual Information Retrieval Interfaces2006clouds dataset del.icio.us information tag tagging taggingsurvey toread visual Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud�s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.Fri Jan 16 11:50:31 CET 2009Journal of Physics A: Mathematical and Theoretical22224016 (7pp)Folksonomies and clustering in the collaborative system CiteULike412008*** citeulike clustering dataset folksonomy network properties We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.Mon Dec 01 15:38:40 CET 2008New York, NY, USACIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining93--102A sparse gaussian processes classification framework for fast tag suggestions2008bibsonomy bookmarking classification dataset ml recommender social tag tagging taggingsurvey toread Thu May 08 12:17:01 CEST 2008Behav Res MethodsNov4547-559Semantic feature production norms for a large set of living and nonliving things372005dataset grounding ol ontology relation semantic toread Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.Fri Dec 14 09:04:20 CET 2007How To Break Anonymity of the Netflix Prize Dataset2006Preis anonymity dataset netflix prize recommender We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge.Fri Jun 23 07:06:19 CEST 2006{UCI} Repository of machine learning databases1998learning data dataset dm mining machine ml uci