SceneRec: Scene-Based Graph Neural Networks for Recommender Systems

TL;DR

SceneRec introduces a novel scene-based graph neural network approach for recommender systems, integrating scene context with collaborative filtering to improve recommendation accuracy.

Contribution

The paper proposes SceneRec, a new model that incorporates scene information into collaborative filtering using scene-based graphs and GNNs, which is a novel approach.

Findings

SceneRec outperforms traditional collaborative filtering methods on real-world datasets.

Incorporating scene context improves recommendation accuracy.

SceneRec effectively learns scene-specific representations for better predictions.

Abstract

Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learning, which aims to project users and items into a low dimensional space to capture collaborative signals. However, the scene information, which has effectively guided many recommendation tasks, is rarely considered in existing collaborative filtering methods. To bridge this gap, we focus on scene-based collaborative recommendation and propose a novel representation model SceneRec. SceneRec formally defines a scene as a set of pre-defined item categories that occur simultaneously in real-life situations and creatively designs an item-category-scene hierarchical structure to build a scene-based graph. In the scene-based graph, we adopt graph neural networks to learn scene-specific representation on each item node, which is further aggregated with latent representation learned from collaborative interactions to make recommendations. We perform extensive experiments on real-world E-commerce datasets and the results demonstrate the effectiveness of the proposed method.