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Active Learning in Recommendation Systems with Multi-level User Preferences
[article]
2018
arXiv
pre-print
While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience. In this work, we study the active learning problem with multi-level user preferences within the collective matrix factorization (CMF) framework. CMF
arXiv:1811.12591v1
fatcat:czcqgie2onfn7blkf5fm4wa45y