Prior-Guided Accuracy-Bias Tradeoff Learning for CTR Prediction in Multimedia Recommendation

Abstract

Although debiasing in multimedia recommendation has shown promising results, most existing work relies on the ability of the model itself to fully disentangle the biased and unbiased information and considers arbitrarily removing all the biases. However, in many business scenarios, it is usually possible to extract a subset of features associated with the biases by means of expert knowledge, i.e., the confounding proxy features. Therefore, in this paper, we propose a novel debiasing framework with confounding proxy priors for the accuracy-bias tradeoff learning in the multimedia recommendation, or CP2Rec for short, in which these confounding proxy features driven by the expert experience are integrated into the model as prior knowledge corresponding to the biases. Specifically, guided by these priors, we use a bias disentangling module with some orthogonal constraints to force the model to avoid encoding biased information in the feature embeddings. We then introduce an auxiliary unbiased loss to synergize with the original biased loss in an accuracy-bias tradeoff module, aiming at recovering the beneficial bias information from the above-purified feature embeddings to achieve a more reasonable accuracy-bias tradeoff recommendation. Finally, we conduct extensive experiments on a public dataset and a product dataset to verify the effectiveness of CR2Rec. In addition, CR2Rec is also deployed on a large-scale financial multimedia recommendation platform in China and achieves a sustained performance gain.