Abstract
When case-based recommender systems use preference-based feedback, we can learn user preferences by using the trade-off relations between the preferred product and the other products in the given domain. In this work, we propose a representation for trade-offs and motivate several mechanisms by which the identified trade-offs can be used in the process of recommendation. We empirically demonstrate the effectiveness of the proposed approaches in three recommendation domains.
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Notes
- 1.
We use the terms cases and products; case base and product domain; features and attributes interchangeably.
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Sekar, A., Ganesan, D., Chakraborti, S. (2018). Why Did Naethan Pick Android over Apple? Exploiting Trade-offs in Learning User Preferences. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_24
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