TY - JOUR AU - McRae, K AU - Cree, G S AU - Seidenberg, M S AU - McNorgan, C T1 - Semantic feature production norms for a large set of living and nonliving things JO - Behav Res Methods PY - 2005/november VL - 37 IS - 4 SP - 547 EP - 559 UR - http://www.ncbi.nlm.nih.gov/pubmed/16629288 M3 - KW - dataset KW - grounding KW - ol KW - ontology KW - relation KW - semantic KW - toread L1 - SN - N1 - Semantic feature production norms for a large set ...[Behav Res Methods. 2005] - PubMed Result N1 - AB - 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. ER - TY - CONF AU - Narayanan, Arvind AU - Shmatikov, Vitaly A2 - T1 - Robust De-anonymization of Large Sparse Datasets T2 - Proc. of the 29th IEEE Symposium on Security and Privacy PB - IEEE Computer Society CY - PY - 2008/05 M2 - VL - IS - SP - 111 EP - 125 UR - http://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf M3 - 10.1109/SP.2008.33 KW - anonymization KW - datamining KW - dataset KW - netflix KW - privacy KW - recommender KW - toread L1 - SN - N1 - N1 - AB - 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. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information. ER - TY - CONF AU - Hassan-Montero, Y. AU - Herrero-Solana, V. A2 - T1 - Improving Tag-Clouds as Visual Information Retrieval Interfaces T2 - InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies PB - CY - PY - 2006/ M2 - VL - IS - SP - EP - UR - http://nosolousabilidad.com/hassan/improving_tagclouds.pdf M3 - KW - clouds KW - dataset KW - del.icio.us KW - information KW - tag KW - tagging KW - taggingsurvey KW - toread KW - visual L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Zubiaga, Arkaitz AU - Fresno, Victor AU - Martinez, Raquel AU - Garcia-Plaza, Alberto P. T1 - Harnessing Folksonomies to Produce a Social Classification of Resources JO - IEEE Transactions on Knowledge and Data Engineering PY - 2012/ VL - 99 IS - PrePrints SP - EP - UR - M3 - http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115 KW - classification KW - delicious KW - folksonomy KW - tagging KW - toread KW - dataset L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Sinha, Arnab AU - Shen, Zhihong AU - Song, Yang AU - Ma, Hao AU - Eide, Darrin AU - Hsu, Bo-June Paul AU - Wang, Kuansan A2 - Gangemi, Aldo A2 - Leonardi, Stefano A2 - Panconesi, Alessandro T1 - An Overview of Microsoft Academic Service (MAS) and Applications. T2 - WWW (Companion Volume) PB - ACM CY - PY - 2015/ M2 - VL - IS - SP - 243 EP - 246 UR - http://dblp.uni-trier.de/db/conf/www/www2015c.html#SinhaSSMEHW15 M3 - KW - MSAC KW - dataset KW - toread L1 - SN - 978-1-4503-3473-0 N1 - N1 - AB - ER - TY - CONF AU - Song, Yang AU - Zhang, Lu AU - Giles, C. Lee A2 - T1 - A sparse gaussian processes classification framework for fast tag suggestions T2 - CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 93 EP - 102 UR - http://portal.acm.org/citation.cfm?id=1458098 M3 - http://doi.acm.org/10.1145/1458082.1458098 KW - bibsonomy KW - bookmarking KW - classification KW - dataset KW - ml KW - recommender KW - social KW - tag KW - tagging KW - taggingsurvey KW - toread L1 - SN - 978-1-59593-991-3 N1 - A sparse gaussian processes classification framework for fast tag suggestions N1 - AB - ER -