Neural_BPR: Multi-processing popularity bias mitigating method in recommendation systems

Abstract

E-commerce recommendations address the problem of information overload, but recent research has identified the phenomenon of popularity bias in recommendation mechanisms. This phenomenon tends to homogenise recommendation results, i.e. popular products are widely recommended and non-popular products are under-exposed. The persistent presence of popularity bias in recommendation systems can lead to several negative consequences: (1) Consumers are unable to attain satisfactory personalized shopping experiences. (2) Small and medium enterprises are deprived of fair competitive opportunities, making survival challenging. (3) The user base of e-commerce platforms dwindles, leading to reduced transaction volumes. In this paper, we propose a multi-process fusion debiasing method. Convolutional neural networks are used to extract feature information to be embedded in the recommendation algorithm for pre-processing, in-processing and post-processing in turn to mitigate popularity bias and simultaneously enhance recommendation utility. The effectiveness of the proposed debiasing method is validated using the Bayesian Personalized Ranking (BPR) recommendation model as an example. Experimental results on two public datasets demonstrate that our model outperforms two baseline models and a state-of-the-art model. Specifically, compared to the traditional BPR model, the alleviation of popularity bias is around 70% to 80%, and the recommendation utility increases by approximately 30% to 50%.