  Recommendation with side information has drawn significant research interest
due to its potential to mitigate user feedback sparsity. However, existing
models struggle with generalization across diverse domains and types of side
information. In particular, three challenges have not been addressed, and they
are (1) the diverse formats of side information, including text sequences. (2)
The diverse semantics of side information that describes items and users from
multi-level in a context different from recommendation systems. (3) The diverse
correlations in side information to measure similarity over multiple objects
beyond pairwise relations. In this paper, we introduce GENET (Generalized
hypErgraph pretraiNing on sidE informaTion), which pre-trains user and item
representations on feedback-irrelevant side information and fine-tunes the
representations on user feedback data. GENET leverages pre-training as a means
to prevent side information from overshadowing critical ID features and
feedback signals. It employs a hypergraph framework to accommodate various
types of diverse side information. During pre-training, GENET integrates tasks
for hyperlink prediction and self-supervised contrast to capture fine-grained
semantics at both local and global levels. Additionally, it introduces a unique
strategy to enhance pre-training robustness by perturbing positive samples
while maintaining high-order relations. Extensive experiments demonstrate that
GENET exhibits strong generalization capabilities, outperforming the SOTA
method by up to 38% in TOP-N recommendation and Sequential recommendation tasks
on various datasets with different side information.
