Mining Semantic Data, User Generated Contents, and Contextual Information for Cross-Domain Recommendation

• Published in: User Modeling, Adaptation, and Personalization pp. 371 - 375
• Main Author: Fernández-Tobías, Ignacio
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013
• Series: Lecture Notes in Computer Science
• Subjects: context-aware recommendations; Cross-domain recommender systems; semantic networks; social tagging
• ISBN: 9783642388439; 3642388434
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-38844-6_42

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.