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Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
We present some of the challenges associated with using genre label information and propose a new way of examining the genre information that we call as the \textit{Genre Spectrum}.
Recommendation under Capacity Constraints
In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i. e., number of seats in a Point-of-Interest (POI) or size of an item's inventory.
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems.
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