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Recommender system always suffers from various recommendation biases,seriously hindering its development. In this light, a series of debias methodshave been proposed in the recommender system, especially for two most commonbiases, i.e., popularity bias and amplified subjective bias. However, exsistingdebias methods usually concentrate on correcting a single bias. Suchsingle-functionality debiases neglect the bias-coupling issue in which therecommended items are collectively attributed to multiple biases. Besides,previous work cannot tackle the lacking supervised signals brought by sparsedata, yet which has become a commonplace in the recommender system. In thiswork, we introduce a disentangled debias variational auto-encoderframework(DB-VAE) to address the single-functionality issue as well as acounterfactual data enhancement method to mitigate the adverse effect due tothe data sparsity. In specific, DB-VAE first extracts two types of extremeitems only affected by a single bias based on the collier theory, which arerespectively employed to learn the latent representation of correspondingbiases, thereby realizing the bias decoupling. In this way, the exact unbiaseduser representation can be learned by these decoupled bias representations.Furthermore, the data generation module employs Pearl's framework to producemassive counterfactual data, making up the lacking supervised signals due tothe sparse data. Extensive experiments on three real-world datasets demonstratethe effectiveness of our proposed model. Besides, the counterfactual data canfurther improve DB-VAE, especially on the dataset with low sparsity.

Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation