Graph Reasoning for Explainable Cold Start Recommendation

TL;DR

This paper introduces GRECS, a graph reasoning framework that improves cold start recommendations by leveraging explicit paths in knowledge graphs, enhancing both accuracy and interpretability.

Contribution

GRECS adapts graph reasoning to cold start scenarios by using explicit user paths, offering a novel approach that enhances recommendation relevance and explainability.

Findings

GRECS outperforms baseline methods on 5 datasets.

GRECS effectively mitigates the cold start problem.

GRECS provides interpretable recommendations.

Abstract

The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS). A common solution involves using Knowledge Graphs (KG) to train entity embeddings or Graph Neural Networks (GNNs). Since KGs incorporate auxiliary data and not just user/item interactions, these methods can make relevant recommendations for cold users or items. Graph Reasoning (GR) methods, however, find paths from users to items to recommend using relations in the KG and, in the context of RS, have been used for interpretability. In this study, we propose GRECS: a framework for adapting GR to cold start recommendations. By utilizing explicit paths starting for users rather than relying only on entity embeddings, GRECS can find items corresponding to users' preferences by navigating the graph, even when limited information about users is available. Our experiments show that GRECS mitigates the cold start problem and outperforms competitive baselines across 5 standard datasets while being explainable. This study highlights the potential of GR for developing explainable recommender systems better suited for managing cold users and items.