Mitigating Extreme Cold Start in Graph-based RecSys through Re-ranking

Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. By exploiting the enriched graph structure, the framework generates embeddings for cold-start users and items, enabling diverse recommendations, containing long tail and unsold items, for both established and new users. We validate the framework's effectiveness on real-world industrial data from TIM S.p.A.