Abstract
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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References
Fernández-Tobías, I., Kaminskas, M., Cantador, I., Ricci, F.: A Generic semantic-based framework for cross-domain recommendation. In: 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 25–32 (2011)
Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 496–503 (2011)
Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I.: Ontology-based identification of music for places. In: 13th International Conference on Information and Communication Technologies in Tourism (to appear, 2013)
Kaminskas, M., Ricci, F.: Location-Adapted music recommendation using tags. In: 19th Intl. Conference on User Modeling, Adaptation, and Personalization, pp. 183–194 (2011)
Li, B.: Cross-domain collaborative filtering: A brief survey. In: 23rd International Conference on Tools with Artificial Intelligence, pp. 1085–1086 (2011)
Loizou, A.: How to recommend music to film buffs: enabling the provision of recommendations from multiple domains. PhD dissertation, Univ. of Southampton (2009)
Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)
Shi, Y., Larson, M., Hanjalic, A.: Tags as bridges between domains: Improving recommendation with tag-induced cross-domain collaborative filtering. In: 19th International Conference on User modeling, Adaption, and Personalization, pp. 305–316 (2011)
Tiroshi, A., Kuflik, T.: Domain ranking for cross domain collaborative filtering. In: 20th Intl. Conference on User Modeling, Adaptation, and Personalization, pp. 328–333 (2012)
Winoto, P., Ya Tang, T.: If you like The devil wears Prada the book, will you also enjoy The devil wears Prada the movie? A study of cross-domain recommendations. New Generation Computing 26(3), 209–225 (2008)
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Fernández-Tobías, I. (2013). Mining Semantic Data, User Generated Contents, and Contextual Information for Cross-Domain Recommendation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_42
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DOI: https://doi.org/10.1007/978-3-642-38844-6_42
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