Abstract
A challenge of Context-Aware Recommender Systems (CARSs) is the cold-start problem, i.e., the usual poor recommendation of new items to new users in new contextual situations. In this research, we aim at solving this problem by developing a switching hybrid CARS, which exploits different context-aware recommendation techniques, each of which has its own strengths and weaknesses, and switches between these techniques depending on the current recommendation situation (i.e., new user, new item and/or new context).
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Braunhofer, M. (2014). Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_44
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DOI: https://doi.org/10.1007/978-3-319-08786-3_44
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