TAI2Vec learns item embeddings by adapting temporal context definitions to each user's interaction pace, either via personalized session segmentation or continuous decay weighting, and shows gains over static baselines on eight datasets.
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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.
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
citing papers explorer
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Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts
TAI2Vec learns item embeddings by adapting temporal context definitions to each user's interaction pace, either via personalized session segmentation or continuous decay weighting, and shows gains over static baselines on eight 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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Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.