Variations in user state embeddings for CMAB recommenders can improve performance more than changing the bandit algorithm, with no embedding or aggregation strategy dominating across datasets.
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Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.
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The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Variations in user state embeddings for CMAB recommenders can improve performance more than changing the bandit algorithm, with no embedding or aggregation strategy dominating across datasets.
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HiMARS: Hybrid multi-objective algorithms for recommender systems
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.