TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference

Zuoli Tang, Zhaoxin Huan, Zihao Li, Shirui Hu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li

Abstract: Recently, the item textual information has been exploited with pre-trained language models (PLMs) to enrich the representations of tail items. The underlying idea is to align the hot items and tail items in terms of the external semantic knowledge covered by the PLM. However, it is non-trivial to eliminate the popularity bias by exploiting the textual semantics. One major obstacle is that the model supervision still counts on the sparse yet binary user behaviors. In the preliminary investigation, we discover that text-based recommendations also suffer from the popularity bias.To this end, we propose a novel self-distillation framework based on a pre-trained language model, named Staple. The proposed Staple consists of two main components: ranker model and recommender model, which are both instantiated as a PLM towards exploiting the item textual semantics. Motivated by the recent success of reinforcement learning with human feedback (RLHF), the proposed Staple aims to recover the relative preference by learning a fair ranker model that can successfully distinguish the preference levels for uninteracted items. Specifically, analogous to the training of large language models (LLMs), we introduce a pre-training and a fair supervised fine-tuning with a decoupled layer to build the ranker model. Then, similar to RLHF for LLM training, we utilize the relative preference information estimated by the ranker over candidate items to complement the learning of the recommender model. We show that this RLHF process can be reformed as an efficient distillation learning process. We conduct extensive experiments on three real-world datasets. In addition to the performance metrics, we employ two additional metrics to measure fairness and debiased performance. The experiments show that our method can significantly improve the item exposure fairness of recommendation and mitigate popularity bias, while also improving the recommendation performance. The source code is available at https://github.com/WHUIR/STAPLE.