Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning

arXiv:2403.00843v1 Announce Type: cross
Abstract: Traditional recommendation setting tends to excessively cater to users' immediate interests and neglect their long-term engagement. To address it, it is crucial to incorporate planning capabilities into the recommendation decision-making process to develop policies that take into account both immediate interests and long-term engagement. Despite Reinforcement Learning (RL) can learn planning capacity by maximizing cumulative reward, the scarcity of recommendation data presents challenges such as instability and susceptibility to overfitting when training RL models from …

abstract arxiv capabilities cs.ai cs.cl cs.ir cs.lg decision engagement language language model large language large language model long-term making planning process recommendation type