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

摘要

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.