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Recommender systems
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#	Item
241	3-4 Development of Dialogue Systems “‘Kyo-no Hanna’ and ‘Kyo no Osusume’” MISU Teruhisa, MIZUKAMI Etsuo, SUGIURA Komei, and IWAHASHI Naoto We developed dialogue systems of “Kyo-no Hanna” and “Kyono Osus Add to Reading List Source URL: www.nict.go.jp Language: English - Date: 2013-01-21 20:47:18 User interfaces User interface techniques Dialog system Systems engineering Spoken dialog system Dialogue Speech recognition Recommender system Speech synthesis Humanâ€“computer interaction Computational linguistics Multimodal interaction
242	USER MODELING ON THE WEB An Exploratory Review of Recommendation Systems By Olav Bjørkøy ([removed]) Add to Reading List Source URL: www.bjorkoy.com Language: English - Date: 2014-07-08 05:02:10 Computing Science Knowledge representation Recommender system Personalization User interface User modeling User model Email Information science User interface techniques Human–computer interaction
243	Short and Tweet: Experiments on Recommending Content from Information Streams Jilin Chen, Rowan Nairn†, Les Nelson†, Michael Bernstein∆, Ed H. Chi† University of Minnesota Add to Reading List Source URL: www-users.cs.umn.edu Language: English - Date: 2010-01-20 19:51:16 Internet search engines Technology Recommender systems Real-time web Text messaging Twitter GroupLens Research Collaborative filtering Cold start World Wide Web Collective intelligence Information science
244	Social Collaborative Filtering for Cold-start Recommendations ∗ Suvash Sedhain Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2014-09-08 21:23:24 Information systems Science Information Collaboration Recommender systems Collaborative filtering Cold start Information filtering system Personalization Information science Collective intelligence Information retrieval
245	An Analysis of Tag-Recommender Evaluation Procedures Stephan Doerfel Robert Jäschke University of Kassel Add to Reading List Source URL: www.kbs.uni-hannover.de Language: English - Date: 2015-04-09 16:46:04 Science Collaboration Collaborative filtering Collective intelligence Social information processing Mathematics Core Information filtering system Algorithm Information science Information retrieval Recommender systems
246	REPORT FROM THE WORKING GROUP ON VERIFICATION (WGIII) Chairperson: K. Holmlund Participants: R. Sarrazin, E. Ozawa, H. Berger, N. Borman, M. Tokuno, J. Gustafsson, M. Das Gupta, A. Stoffelen, P. Menzel, M. Goldsworthy, P Add to Reading List Source URL: cimss.ssec.wisc.edu Language: English - Date: 2010-04-06 09:04:21 Validity Atmospheric sciences Food safety Quality management Systems engineering Verification and validation Data assimilation BUFR Recommender system Statistics Science Pharmaceutical industry
247	Social Affinity Filtering Recommendation through Fine-grained Analysis of User Interactions and Activities Suvash Sedhain Scott Sanner Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2013-09-06 02:49:54 Software Collective intelligence Information systems Web 2.0 Social media Recommender system Collaborative filtering Social networking service Internet privacy World Wide Web Computing Social information processing
248	Social Collaborative Filtering for Cold-start Recommendations ∗ Suvash Sedhain Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2014-08-17 19:50:32 Information systems Science Information Collaboration Recommender systems Collaborative filtering Cold start Information filtering system Personalization Information science Collective intelligence Information retrieval
249	7. Classification Decision Makers Contents Summary Who currently classifies content? Films, computer games and publications Television content Add to Reading List Source URL: www.alrc.gov.au Language: English - Date: 2012-03-01 00:57:28 Film Censorship in Australia Cinema of the United States Motion Picture Association of America film rating system Recommender system Video game content ratings systems Australian Classification Board British Board of Film Classification Motion picture rating systems Censorship Motion Picture Association of America
250	User Model User-Adap Inter[removed]:101–123 DOI[removed]s11257[removed]x ORIGINAL PAPER Recommender systems: from algorithms to user experience Add to Reading List Source URL: blog.namics.com Language: English - Date: 2013-02-14 03:12:07 Recommender systems Information retrieval Information systems GroupLens Research Collaborative filtering Cold start Information filtering system MovieLens Personalization Information science Human–computer interaction Collective intelligence

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