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Recommender systems
Results: 381

#	Item
281	An Analysis of Recommender Algorithms for Online News Doychin Doychev, Aonghus Lawlor, Rachael Rafter, and Barry Smyth Insight Centre for Data Analytics, University College Dublin Belfield, Dublin 4, Ireland. {firstname. Add to Reading List Source URL: ceur-ws.org Language: English - Date: 2014-07-25 04:51:02 Collective intelligence Information retrieval Collaborative filtering Science Information HTTP cookie GroupLens Research Recommender systems Information science Social information processing
282	A Hybrid Multi-Strategy Recommender System Using Linked Open Data Petar Ristoski1 and Eneldo Loza Menc´ıa2 and Heiko Paulheim1 1 University of Mannheim, Germany Research Group Data and Web Science Add to Reading List Source URL: ub-madoc.bib.uni-mannheim.de Language: English - Date: 2014-10-15 04:50:36 Collective intelligence Social information processing Information retrieval Science Collaborative filtering Statistics Linked data R Information science Recommender systems Collaboration
283	Optimizing and Evaluating Stream-based News Recommendation Algorithms Sebastian Werner1 and Andreas Lommatzsch2 1 2 Add to Reading List Source URL: ceur-ws.org Language: English - Date: 2014-07-25 04:50:00 Recommender systems Social information processing Collaboration Information systems Cold start Collaborative filtering Cluster analysis HTTP cookie Web portal Statistics Collective intelligence Information science
284	Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao Web Information Systems, TU Delft PO Box 5031, 2600 GA Delft, the Netherlan Add to Reading List Source URL: fabianabel.de Language: English - Date: 2011-05-27 04:40:09 World Wide Web Twitter Collective intelligence Knowledge representation Social media Personalization Hashtag User model Recommender system Information science Information Information retrieval
285	Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao Web Information Systems, TU Delft PO Box 5031, 2600 GA Delft, the Netherlan Add to Reading List Source URL: www.websci11.org Language: English - Date: 2011-05-24 10:32:48 World Wide Web Twitter Collective intelligence Knowledge representation Social media Personalization Hashtag User model Recommender system Information science Information Information retrieval
286	Microsoft Word - crowdrec_workshop_v7 Add to Reading List Source URL: crowdrec2013.noahlab.com.hk Language: English - Date: 2013-10-04 00:02:28 Social information processing Collaboration Recommender systems Cold start Information filtering system Crowdsourcing Human-based computation GroupLens Research Collaborative filtering Information systems Collective intelligence Information science
287	Bayesian Nonparametric Poisson Factorization for Recommendation Systems Prem Gopalan Princeton University Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2014-03-17 12:44:23 Variational Bayesian methods Mixture model Poisson distribution Collaborative filtering Dirichlet process Bayesian inference Recommender system Gamma distribution Markov chain Statistics Bayesian statistics Expectation–maximization algorithm
288	GeoEcho: Inferring User Interests from Geotag Reports in Network Traffic Ning Xia∗ , Stanislav Miskovic† , Mario Baldi† , Aleksandar Kuzmanovic∗ , Antonio Nucci† ∗ Northwestern University [removed]hw Add to Reading List Source URL: networks.cs.northwestern.edu Language: English - Date: 2014-05-28 17:08:01 Entertainment Personal life Geographic information systems New Zealand culture Poi spinning Point of interest Geotagging Social networking service Recommender system GPS Technology Satellite navigation systems
289	R Foundations and Trends in Human–Computer Interaction Vol. 4, No[removed]–173 c 2011 M. D. Ekstrand, J. T. Riedl and J. A. Konstan Add to Reading List Source URL: files.grouplens.org Language: English - Date: 2011-09-19 15:41:05 Recommender systems Collective intelligence Social information processing GroupLens Research Collaborative filtering Personalization Cold start Guided selling Information science Information retrieval Human–computer interaction
290	Enhancement of the Neutrality in Recommendation Toshihiro Kamishima, Shotaro Akaho, and Hideki Asoh National Institute of Advanced Industrial Science and Technology (AIST) AIST Tsukuba Central 2, Umezono 1-1-1, Add to Reading List Source URL: recex.ist.tugraz.at Language: English - Date: 2012-09-08 17:28:10 Information Science Recommender systems Social information processing Collective intelligence GroupLens Research Collaborative filtering Filter bubble Information filtering system Information science Human–computer interaction Information retrieval

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