Textual data are commonly used as auxiliary information for modeling user preference nowadays. While many prior works utilize user reviews for rating prediction, few focus on top-N recommendation, and even few try to incorporate item textual contents such as title and description. Though delivering promising performance for rating prediction, we empirically find that many review-based models cannot perform comparably well on top-N recommendation. Also, user reviews are not available in some recommendation scenarios, while item textual contents are more prevalent. On the other hand, recent graph convolutional network (GCN) based models demonstrate state-of-the-art performance for top-N recommendation. Thus, in this work, we aim to further improve top-N recommendation by effectively modeling both item textual content and high-order connectivity in user-item graph. We propose a new model named Attentive Graph-based Text-aware Recommendation Model (AGTM). Extensive experiments are provided to justify the rationality and effectiveness of our model design.

翻译：文本数据如今常被用作辅助信息来建模用户偏好。虽然许多先前的工作利用用户评论进行评分预测，但很少有研究关注Top-N推荐，更少有尝试融入商品文本内容（如标题和描述）。尽管评论模型在评分预测中展现出令人瞩目的性能，但我们通过实验发现，许多基于评论的模型在Top-N推荐中表现欠佳。此外，用户评论在某些推荐场景中不可用，而商品文本内容则更为普遍。另一方面，基于图卷积网络（GCN）的模型在Top-N推荐中展现出最先进的性能。因此，在本工作中，我们旨在通过有效建模商品文本内容以及用户-商品图中的高阶连通性，进一步改进Top-N推荐。我们提出了一种名为基于注意力图的文本感知推荐模型（AGTM）的新模型。通过大量实验证明了我们模型设计的合理性和有效性。