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.

翻译：基于图神经网络（GNN）的模型已被广泛研究用于推荐系统，因为它们能够准确提取高质量推荐系统所需的高阶协同信号。然而，这些模型忽略了通过负反馈获得的宝贵信息，具体体现在两个方面：（1）不同用户可能对同一项目持有相反的反馈，这阻碍了GNN中的最优信息传播；（2）即使某个项目与用户偏好存在巨大偏差，用户仍可能选择它并给出负面评分。本文提出了一种负反馈感知推荐模型（NFARec），该模型最大程度地利用了负反馈。为了沿最优路径有效将信息传递至多跳邻居，NFARec采用了一种反馈感知相关性，引导超图卷积（HGC）学习用户的结构表示。此外，NFARec基于Transformer霍克斯过程引入了一个辅助任务——预测下一次交互的反馈情感极性（即正面或负面）。该任务通过从用户历史序列反馈模式中学习所表达的情感并预测未来交互，有助于理解用户。大量实验表明，NFARec优于竞争基线模型。我们的源代码和数据已发布于https://github.com/WangXFng/NFARec。