The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity on par with accuracy and garnering noteworthy attention from academic circles and industry practitioners. This research explores the realm of diversified RecSys within the intricate context of knowledge graphs (KG). These KGs act as repositories of interconnected information concerning entities and items, offering a propitious avenue to amplify recommendation diversity through the incorporation of insightful contextual information. Our contributions include introducing an innovative metric, Entity Coverage, and Relation Coverage, which effectively quantifies diversity within the KG domain. Additionally, we introduce the Diversified Embedding Learning (DEL) module, meticulously designed to formulate user representations that possess an innate awareness of diversity. In tandem with this, we introduce a novel technique named Conditional Alignment and Uniformity (CAU). It adeptly encodes KG item embeddings while preserving contextual integrity. Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.

翻译：推荐系统（RecSys）领域通过利用用户的历史交互行为来提升准确率已得到广泛研究。然而，这种对准确率的持续追求常常导致多样性降低，最终形成广为人知的“信息茧房”现象。多样化推荐系统作为应对策略应运而生，将多样性置于与准确率同等重要的地位，并获得了学术界和工业界的广泛关注。本研究探索了知识图谱（KG）复杂语境下多样化推荐系统的领域。这些知识图谱作为实体与项目间相互关联信息的存储库，通过融入富有洞察力的上下文信息为提升推荐多样性提供了可行途径。我们的贡献包括引入创新性指标——实体覆盖率和关系覆盖率，有效量化了知识图谱领域的多样性。同时，我们提出多样化嵌入学习（DEL）模块，该模块经过精心设计，能够生成具有内在多样性意识的用户表征。与此相辅相成的是，我们引入名为条件对齐与一致性（CAU）的新型技术，该技术能在保持上下文完整性的同时巧妙编码KG项目嵌入。总体而言，我们的贡献标志着在知识图谱赋能推荐系统的范式下，向着拓展推荐多样性全景迈出了重要一步。