Understanding user preference is of key importance for an effective recommender system. For comprehensive user profiling, many efforts have been devoted to extract user feature-level preference from the review information. Despite effectiveness, existing methods mostly assume linear relationships among the users, items, and features, and the collaborative information is usually utilized in an implicit and insufficient manner, which limits the recommender capacity in modeling users’ diverse preferences. For bridging this gap, in this article, we propose to formulate user feature-level preferences by a neural signed hypergraph and carefully design the information propagation paths for diffusing collaborative filtering signals in a more effective manner. By taking the advantages of the neural model’s powerful expressiveness, the complex relationship patterns among users, items, and features are sufficiently discovered and well utilized. By infusing graph structure information into the embedding process, the collaborative information is harnessed in a more explicit and effective way. We conduct comprehensive experiments on real-world datasets to demonstrate the superiorities of our model.

中文翻译：

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带有签名超图卷积网络的神经特征感知推荐

了解用户偏好对于有效的推荐系统至关重要。对于全面的用户分析，已经付出了很多努力来从评论信息中提取用户特征级别的偏好。尽管有效，但现有方法大多假设用户、项目和特征之间的线性关系，并且协作信息通常以隐含和不充分的方式利用，这限制了推荐器在建模用户多样化偏好方面的能力。为了弥合这一差距，在本文中，我们建议通过神经签名超图来制定用户特征级别的偏好，并仔细设计信息传播路径，以更有效地扩散协同过滤信号。通过利用神经模型强大的表现力，用户、项目和特征之间的复杂关系模式得到充分发现和充分利用。通过将图结构信息注入嵌入过程，可以更明确和有效地利用协作信息。我们对现实世界的数据集进行了全面的实验，以证明我们模型的优越性。