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