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：推荐系统领域长期以来致力于通过利用用户历史交互行为提升推荐精度。然而，这种对精度的持续追求往往导致多样性降低，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，将多样性置于与精度同等重要的地位，并获得了学术界与工业界的广泛关注。本研究探索了知识图谱复杂语境下的多样化推荐系统。知识图谱作为实体与物品间关联信息的存储库，通过引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：提出创新性评估指标——实体覆盖度与关系覆盖度，有效量化知识图谱领域的多样性；设计多样化嵌入学习模块，该模块通过精密构建具备内在多样性感知能力的用户表征；同时提出名为条件对齐与一致性的新技术，在保持上下文完整性的前提下对知识图谱物品嵌入进行高效编码。综合而言，我们的研究成果标志着在知识图谱驱动的推荐系统范式下，向提升推荐多样性全景迈出了重要一步。