HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems

Contextual situations, such as having dinner at a restaurant on Friday with the spouse, became a useful mechanism to represent context in context-aware recommender systems (CARS). Prior research has shown important advantages of using latent embedding representation approaches to model contextual information in the Euclidean space leading to better recommendations. However, these traditional approaches have major challenges with construction of proper embeddings of hierarchical structures of contextual information, as well as with interpretations of the obtained representations that would be useful for managers. To address these problems, we propose the HyperCARS method that models hierarchical contextual situations in the latent hyperbolic space. HyperCARS combines hyperbolic embeddings with hierarchical clustering to construct contextual situations, which allows to loosely couple the contextual modeling component with recommendation algorithms and therefore provides flexibility to use a broad range of previously developed recommendation algorithms. We demonstrate empirically that the proposed hyperbolic embedding approach better captures the hierarchical nature of context than its Euclidean counterpart and produces hierarchical contextual situations that are more distinct and better separated at multiple hierarchical levels. We also demonstrate that hyperbolic contextual situations lead to better context-aware recommendations in terms of standard recommendation metrics and to better interpretability of the resulting hierarchical contextual situations. Because hyperbolic embeddings can also be used in many other applications besides CARS, in this paper, we propose the latent embeddings representation framework that systematically classifies prior work on embeddings and identifies novel research streams for hyperbolic embeddings across information systems applications.