Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-based recommender systems, existing methods are prone to suboptimal performance due to the following two drawbacks: 1) current KG-based methods over-emphasize the heterogeneous structural information within a KG and overlook the underlying semantics of its connections, hindering the recommender from distilling the explicit user preferences; and 2) the inherent incompleteness of a KG (i.e., missing facts, relations and entities) will deteriorate the information extracted from KG and weaken the representation learning of recommender systems. To tackle the aforementioned problems, we investigate the potential of jointly incorporating the structural and semantic information within a KG to model user preferences in finer granularity. A new framework for KG-based recommender systems, namely \textit{K}nowledge \textit{I}nfomax \textit{R}ecommender \textit{S}ystem with \textit{C}ontrastive \textit{L}earning (KIRS-CL) is proposed in this paper. Distinct from previous KG-based approaches, KIRS-CL utilizes structural and connectivity information with high-quality item embeddings learned by encoding KG triples with a pre-trained language model. These well-trained entity representations enable KIRS-CL to find the item to recommend via the preference connection between the user and the item. Additionally, to improve the generalizability of our framework, we introduce a contrastive warm-up learning strategy, making it capable of dealing with both warm- and cold-start recommendation scenarios. Extensive experiments on two real-world datasets demonstrate remarkable improvements over state-of-the-art baselines.

翻译：知识图谱已成为辅助推荐系统深入理解用户偏好的重要辅助信息。尽管基于知识图谱的推荐系统近年来取得进展，现有方法仍因以下两个缺陷而性能欠佳：1）当前基于知识图谱的方法过度强调异构结构信息，忽视连接关系的潜在语义，阻碍推荐系统提炼显式用户偏好；2）知识图谱固有的不完整性（即缺失事实、关系和实体）将劣化从图谱中提取的信息，削弱推荐系统的表示学习能力。针对上述问题，我们探究联合知识图谱结构信息与语义信息以更细粒度建模用户偏好的潜力。本文提出基于知识图谱的推荐系统新框架——基于对比学习的知识信息最大化推荐系统（KIRS-CL）。与以往基于知识图谱的方法不同，KIRS-CL通过预训练语言模型编码知识图谱三元组学习高质量物品嵌入，利用结构与连通性信息。这些训练完善的实体表示使KIRS-CL能通过用户与物品的偏好连接定位推荐目标。此外，为提升框架泛化能力，我们引入对比预热学习策略，使其能同时处理冷启动与热启动推荐场景。在两个真实数据集上的大量实验表明，该方法相比现有最优基线取得了显著改进。