Geometric Interaction Augmented Graph Collaborative Filtering

TL;DR

This paper introduces a hybrid geometric space model for graph-based collaborative filtering, combining Euclidean and hyperbolic geometries to better capture complex interaction patterns and improve recommendation accuracy.

Contribution

It proposes a novel hybrid geometric space approach that integrates Euclidean and hyperbolic geometries for more expressive user-item interaction modeling.

Findings

Outperforms existing models on public datasets

Effectively captures complex topological interaction patterns

Demonstrates improved recommendation accuracy

Abstract

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.