Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
This provides practical guidance for researchers and practitioners on applying hyperbolic embeddings in recommender systems, though it is incremental.
This paper analyzes when hyperbolic embeddings outperform Euclidean embeddings in recommender systems, finding they work best with specific models and datasets, and introduces HSCML which achieves state-of-the-art performance.
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space should be considered. To fill this gap, This paper provides theoretical analysis and empirical results on when and where to use hyperbolic space and hyperbolic embeddings in recommender systems. Specifically, we answer the questions that which type of models and datasets are more suited for hyperbolic space, as well as which latent size to choose. We evaluate our answers by comparing the performance of Euclidean space and hyperbolic space on different latent space models in both general item recommendation domain and social recommendation domain, with 6 widely used datasets and different latent sizes. Additionally, we propose a new metric learning based recommendation method called SCML and its hyperbolic version HSCML. We evaluate our conclusions regarding hyperbolic space on SCML and show the state-of-the-art performance of hyperbolic space by comparing HSCML with other baseline methods.