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Your search keyword '"RECOMMENDER systems"' showing total 3 results
Start Over Descriptor "RECOMMENDER systems" Topic bayesian classifier
3 results on '"RECOMMENDER systems"'

1. Two Approaches to the Dataset Interlinking Recommendation Problem

Author

Rabello Lopes, Giseli, Paes Leme, Luiz André P., Pereira Nunes, Bernardo, Casanova, Marco Antonio, Dietze, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Benatallah, Boualem, editor, Bestavros, Azer, editor, Manolopoulos, Yannis, editor, Vakali, Athena, editor, and Zhang, Yanchun, editor

Published
2014
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2. Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationships.

Author

Huang, Cheng-Lung

Subjects
*PERSONAL information management, *SOCIAL networks, *ONLINE social networks, *INTERNET users, *RADIO frequency identification systems
Abstract

Personal information management enables users to manage and classify information via the social tagging. The personal information management platform has recently successfully adopted social networks, enabling users to conveniently share their preferences of information with each other. The emerging social networks generate new concepts for designing modern recommender systems in personal information management and sharing platforms. To design a recommender mechanism for the personal information management and sharing platforms, this work incorporates tag-based personalized interest and social network relationships into a modified Bayesian probability model. The proposed system is demonstrated with experimental datasets obtained from a popular social resource sharing website. The performances of the proposed system are evaluated based on the word2vec word embedding model. Experimental results indicate that incorporating social network information and personalized tag-based preference with the Bayesian model can improve the recommendation quality for social information sharing websites. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Two approaches to the dataset interlinking recommendation problem

Author

Lopes, Giseli Rabello, Leme, Luiz André P. Paes, Pereira Nunes, Bernardo, Casanova, Marco Antonio, Dietze, Stefan, Benatallah, Boualem, Bestavros, Azer, Manolopoulos, Yannis, Vakali, Athena, and Zhang, Yanchun

Subjects
Computer science, Process (engineering), Exploratory search, Linked datum, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Bayesian probability, Recommender system, computer.software_genre, Machine learning, Social networks, Naive Bayes classifier, Dewey Decimal Classification::000 | Allgemeines, Wissenschaft::000 | Informatik, Wissen, Systeme::004 | Informatik, Bayesian classifier, Recommender systems, Social network analysis, Data interlinking, Classification (of information), business.industry, Web of datum, Social networking (online), Real-world datasets, Linked data, Rank scores, Rank score, ComputingMethodologies_PATTERNRECOGNITION, Bayesian networks, Linked Data, Data mining, Artificial intelligence, ddc:004, business, computer
Abstract

Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_25. EC/FP7/LinkedUp CNPq/160326/2012-5 CNPq/303332/2013-1 CNPq/557128/2009-9 FAPERJ/E-26/170028/2008 FAPERJ/E-26/103.070/2011 FAPERJ/E-26/101.382/2014 CAPES/1410827

Published
2014

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