  Graph neural network (GNN)-based models have been extensively studied for
recommendations, as they can extract high-order collaborative signals
accurately which is required for high-quality recommender systems. However,
they neglect the valuable information gained through negative feedback in two
aspects: (1) different users might hold opposite feedback on the same item,
which hampers optimal information propagation in GNNs, and (2) even when an
item vastly deviates from users' preferences, they might still choose it and
provide a negative rating. In this paper, we propose a negative feedback-aware
recommender model (NFARec) that maximizes the leverage of negative feedback. To
transfer information to multi-hop neighbors along an optimal path effectively,
NFARec adopts a feedback-aware correlation that guides hypergraph convolutions
(HGCs) to learn users' structural representations. Moreover, NFARec
incorporates an auxiliary task - predicting the feedback sentiment polarity
(i.e., positive or negative) of the next interaction - based on the Transformer
Hawkes Process. The task is beneficial for understanding users by learning the
sentiment expressed in their previous sequential feedback patterns and
predicting future interactions. Extensive experiments demonstrate that NFARec
outperforms competitive baselines. Our source code and data are released at
https://github.com/WangXFng/NFARec.
