SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems

TL;DR

SEP-GCN introduces a graph-based recommendation model that leverages pairs of contextually similar edges, such as temporal or spatial proximity, to enhance long-range information propagation and improve personalization in location-based recommender systems.

Contribution

The paper presents SEP-GCN, a novel framework that utilizes pairs of similar interaction edges to enrich graph structure and enhance recommendation accuracy, especially in sparse or dynamic settings.

Findings

SEP-GCN outperforms baseline models in predictive accuracy.

The model demonstrates robustness in sparse and dynamic environments.

Edge similarity augmentation improves long-range information flow.

Abstract

Recommender systems play a crucial role in enabling personalized content delivery amidst the challenges of information overload and human mobility. Although conventional methods often rely on interaction matrices or graph-based retrieval, recent approaches have sought to exploit contextual signals such as time and location. However, most existing models focus on node-level representation or isolated edge attributes, underutilizing the relational structure between interactions. We propose SEP-GCN, a novel graph-based recommendation framework that learns from pairs of contextually similar interaction edges, each representing a user-item check-in event. By identifying edge pairs that occur within similar temporal windows or geographic proximity, SEP-GCN augments the user-item graph with contextual similarity links. These links bridge distant but semantically related interactions, enabling improved long-range information propagation. The enriched graph is processed via an edge-aware convolutional mechanism that integrates contextual similarity into the message-passing process. This allows SEP-GCN to model user preferences more accurately and robustly, especially in sparse or dynamic environments. Experiments on benchmark data sets show that SEP-GCN consistently outperforms strong baselines in both predictive accuracy and robustness.