D ebias GAN : Eliminating Position Bias in News Recommendation with Adversarial Learning (2022)

Abstract: Click behaviors are widely used for learning news recommendation models, but they are heavily affected by the biases brought by the news display positions. It is important to remove position biases to train unbiased recommendation model and capture unbiased user interest. In this paper, we propose a news recommendation method named DebiasGAN that can effectively alleviate position biases via adversarial learning. The core idea is modeling the personalized effect of position bias on click behaviors in a candidate-aware way, and learning debiased candidate-aware user embeddings from which the position information cannot be discriminated. More specifically, we use a bias-aware click model to capture the effect of position bias on click behaviors, and use a bias-invariant click model with random candidate positions to estimate the ideally unbiased click scores. We apply adversarial learning to the embeddings learned by the two models to help the bias-invariant click model capture debiased user interest. Experimental results on two real-world datasets show that DebiasGAN effectively improves news recommendation by eliminating position biases.

## introduction accurate news recommendation is critical for improving users' online news reading experience @xcite . existing news recommendation methods mainly use users' news click behaviors for interest inference and model training @xcite @xcite @xcite @xcite . however, news click behaviors are heavily influenced by position biases, i.e., the positions of news when displayed on a webpage @xcite @xcite . since top ranked news are more likely to be clicked than those displayed inconspicuously (baeza-yates, 2018), models directly learned on click data may be inaccurate in targeting user interest @xcite . most existing studies on eliminating position biases in recommendation follow two popular ways @xcite @xcite . the first one is propensity weight, which is a canonical way by weighting the training data based on the influence of position bias @xcite , which can be estimated by randomly ranked data @xcite or regression-based models @xcite . the second one is click model, which aims to simulate the generative process of click behaviors @xcite . it is combined with deep recommendation models by taking positional information as the inputs to disentangle position bias and positionindependent user interest @xcite . however, existing methods usually model position bias in a non-personalized way without considering user characteristics. in fact, the same position may generate different impacts on different users due to their diverse click preferences. for example, some users prefer to skip the top-displayed content @xcite . thus, personalized bias modeling can help better model and eliminate the effects of position bias. in this paper, we propose a news recommendation method named debiasgan foot_0 , which effectively eliminates position biases in news recommendation via adversarial learning. the core idea of our method is two-fold, i.e., using candidate-aware click models to capture the personalized influence of position biases, and using adversarial learning to enforce the candidate-aware user representations learned on real positions to be indistinguishable from those learned on random positions. more specifically, we use a bias-aware click model to capture position biases and a bias-invariant click model with randomized positions of candidate news to estimate the ideally unbiased click scores. both models consider the interactions between positions news encoder transformer candidate news clicked news … … … 𝒓𝒓 1 𝒓𝒓 2 𝒓𝒓 𝑁𝑁 𝐷𝐷 1 𝐷𝐷 2 𝐷𝐷 𝑁𝑁 𝐷𝐷 𝑐𝑐 dense � 𝑦𝑦 bias-aware click score news encoder news encoder news encoder … 𝒉𝒉 1 𝒉𝒉 2 𝒉𝒉 𝑁𝑁 𝑝𝑝1 𝑝𝑝𝟐𝟐 𝑝𝑝𝑁𝑁 + + + 𝑝𝑝𝑐𝑐 + 𝑝𝑝𝑝𝑐𝑐 + candidate-aware attention × × candidate-aware attention dense discriminator 𝑧𝑧 � 𝑦𝑦 bias-invariant click score random position 𝒆𝒆 1 𝒆𝒆 2 𝒆𝒆 𝑁𝑁 𝒆𝒆 𝑐𝑐 𝒆𝒆𝑝 𝑐𝑐 𝒓𝒓 c 𝒅𝒅 𝑐𝑐 𝒅𝒅𝑝 𝑐𝑐 𝒖𝒖 𝒖𝒖𝑝 displayed position adversarial label bias-aware user interest representation bias-invariant user interest representation figure 1: model architecture of debiasgan. of candidate news and clicked news to achieve personalized bias modeling. since the bias information is coupled with candidate-aware user interest in the personalized click models, we propose to apply adversarial learning to the hidden representations encoded by the two click models to encourage the bias-invariant click model to capture debiased user interest in candidate news. experiments on two real-world datasets show that debiasgan can effectively reduce position biases to improve the accuracy of news recommendation, especially on the unbiased dataset generated by random ranking. ## debiasgan the architecture of debiasgan is shown in fig. 1 . it contains a bias-aware click model that captures the position bias effect on click behaviors and a bias-invariant click model to model bias-invariant user interest on candidate news. both models can consider the interactions between user behaviors and candidate news to learn candidate-aware user representations. their details are introduced below. ## conclusion in this paper, we propose a news recommendation method named debiasgan that can eliminate position biases via adversarial learning. we propose to model the interactions between user behavior and position biases to achieve personalized bias model-ing in click prediction. in addition, we propose an adversarial debiasing method to help infer unbiased user interests in candidate news. experiments on two real-world datasets validate that debiasgan can effectively improve news recommendation performance via position debiasing.