HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

TL;DR

This paper introduces HCGR, a hyperbolic contrastive graph learning model for session-based recommendation that captures hierarchical item relationships more effectively than Euclidean methods, leading to improved recommendation accuracy.

Contribution

The paper proposes a novel hyperbolic space framework with adaptive hyperbolic attention and contrastive learning for session-based recommendation, addressing limitations of Euclidean graph methods.

Findings

HCGR outperforms state-of-the-art baselines on four datasets.

The hyperbolic space effectively captures hierarchical item relationships.

Contrastive learning improves item representation quality.

Abstract

Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful kind of way, which generally extract item information by message aggregation under Euclidean space. However, such methods can't effectively extract the hierarchical information contained among consecutive items in a session, which is critical to represent users' preferences. In this paper, we present a hyperbolic contrastive graph recommender (HCGR), a principled session-based recommendation framework involving Lorentz hyperbolic space to adequately capture the coherence and hierarchical representations of the items. Within this framework, we design a novel adaptive hyperbolic attention computation to aggregate the graph message of each user's preference in a session-based behavior sequence. In addition, contrastive learning is leveraged to optimize the item representation by considering the geodesic distance between positive and negative samples in hyperbolic space. Extensive experiments on four real-world datasets demonstrate that HCGR consistently outperforms state-of-the-art baselines by 0.43$\%$-28.84$\%$ in terms of $HitRate$, $NDCG$ and $MRR$.