The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the continuous aspect of recommendation environments, these systems increasingly rely on contextual multi-armed bandits (CMAB) to deliver personalized and real-time suggestions. A critical yet underexplored component in these systems is the representation of user state, which typically encapsulates the user's interaction history and is deeply correlated with the model's decisions and learning. In this paper, we investigate the impact of different embedding-based state representations derived from matrix factorization models on the performance of trad...
| Authors | Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas et al. |
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| Primary category | cs.IR |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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