DeepAI AI Chat
Log In Sign Up

Geometric Interaction Augmented Graph Collaborative Filtering

by   Yiding Zhang, et al.
Beijing University of Posts and Telecommunications
Central South University

Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests. Conventionally, the embeddings of users and items are defined in the Euclidean spaces, along with the propagation on the interaction graphs. Meanwhile, recent works point out that the high-order interactions naturally form up the tree-likeness structures, which the hyperbolic models thrive on. However, the interaction graphs inherently exhibit the hybrid and nested geometric characteristics, while the existing single geometry-based models are inadequate to fully capture such sophisticated topological patterns. In this paper, we propose to model the user-item interactions in a hybrid geometric space, in which the merits of Euclidean and hyperbolic spaces are simultaneously enjoyed to learn expressive representations. Experimental results on public datasets validate the effectiveness of our proposal.


page 1

page 2

page 3

page 4


Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to pr...

Heterogeneous Graph Collaborative Filtering

Graph-based collaborative filtering (CF) algorithms have gained increasi...

HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation

Due to the naturally power-law distributed nature of user-item interacti...

HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization

In large-scale recommender systems, the user-item networks are generally...

HICF: Hyperbolic Informative Collaborative Filtering

Considering the prevalence of the power-law distribution in user-item ne...

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation

Knowledge graph (KG) plays an increasingly important role to improve the...

Collaborative Residual Metric Learning

In collaborative filtering, distance metric learning has been applied to...