AI Research
Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems
Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often involves constructing slates -- ordered lists of items -- that must satisfy multiple objectives beyond relevance, such as constrain...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a novel decoding method for generative recommender systems, allowing them to optimize for multiple objectives like fairness and relevance simultaneously at inference time. This bypasses the need for costly model retraining, making it practical for large-scale systems and improving user satisfaction without compromising other constraints.
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