Knowledge Graph Context-Enhanced Diversified Recommendation
Xiaolong Liu Liangwei Yang Zhiwei Liu Mingdai Yang Chen Wang Hao Peng Philip S. Yu
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.