Dual Debiasing in LLM-based Recommendation

Large language models (LLMs) have been widely applied in recommender systems, achieving remarkable success. However, LLM-based recommendation (LR) suffers from more severe popularity bias than conventional recommendation (CR), stemming from both training and inference stages. In this paper, we propose a novel debiasing method for LR, which performs debiasing in such two stages, so termed as Dual Debiasing in LR (D²LR). Concretely, in the training stage, we conduct token-wise inverse propensity score weighting to force the LLM to pay more attention on unpopular tokens. In the inference stage, we train a more biased CR model by increasing the weights of popular items, which adjusts the generation probability of corresponding tokens according to its scores for items, hoping to suppress the excessive generation of popular tokens. Experiments conducted on three real-world datasets validate the effectiveness of our D²LR in mitigating popularity bias in LR.