Mining semantic data, user generated contents, and contextual information for cross-domain recommendation
Author
Fernández-Tobías, IgnacioEntity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2013Citation
10.1007/978-3-642-38844-6_42
User Modeling, Adaptation, and Personalization: 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings. Lecture Notes in Computer Science, Volumen 7899. Springer, 2013. 371-375
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-38843-9 (print); 978-3-642-38844-6 (online)DOI
10.1007/978-3-642-38844-6_42Editor's Version
http://dx.doi.org/10.1007/978-3-642-38844-6_42Subjects
Context-aware recommendations; Cross-domain recommender systems; Semantic networks; Social tagging; Informática; TelecomunicacionesNote
Proceedings of 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38844-6_42
Rights
© Springer-Verlag Berlin Heidelberg 2013Abstract
Cross-domain recommender systems suggest items in a target domain by exploiting user preferences and/or domain knowledge available in a source domain. In this thesis we aim to develop a framework for cross-domain recommendation capable of mining heterogeneous sources of information such as semantically annotated data, user generated contents, and contextual signals. For this purpose, we investigate a number of approaches to extract, process, and integrate knowledge for linking distinct domains, and various models that exploit such knowledge for making effective recommendations across domains.
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