Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning

arXiv:2403.04875v1 Announce Type: cross
Abstract: Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG. These models treat items as tokens and then utilise a score-and-rank approach (Top-K strategy), where the model first computes item scores and then ranks them according to this score. While this approach works well for accuracy-based metrics, it is hard to use it for optimising more complex beyond-accuracy metrics such as …

abstract accuracy art arxiv beyond cs.ir cs.lg metrics performance recommendation reinforcement reinforcement learning state strategy tokens transformer transformer models type