  Recommender models aimed at mining users' behavioral patterns have raised
great attention as one of the essential applications in daily life. Recent work
on graph neural networks (GNNs) or debiasing methods has attained remarkable
gains. However, they still suffer from (1) over-smoothing node embeddings
caused by recursive convolutions with GNNs, and (2) the skewed distribution of
interactions due to popularity and user-individual biases. This paper proposes
a contextualized and debiased recommender model (CaDRec). To overcome the
over-smoothing issue, we explore a novel hypergraph convolution operator that
can select effective neighbors during convolution by introducing both
structural context and sequential context. To tackle the skewed distribution,
we propose two strategies for disentangling interactions: (1) modeling
individual biases to learn unbiased item embeddings, and (2) incorporating item
popularity with positional encoding. Moreover, we mathematically show that the
imbalance of the gradients to update item embeddings exacerbates the popularity
bias, thus adopting regularization and weighting schemes as solutions.
Extensive experiments on four datasets demonstrate the superiority of the
CaDRec against state-of-the-art (SOTA) methods. Our source code and data are
released at https://github.com/WangXFng/CaDRec.
