Warmth Propagation: A Lightweight Content-Aware Approach to Cold-Start Recommendation

In this paper we propose Warmth Propagation, a novel approach to mitigating the cold-start problem in recommender systems. It relies on content data to compute pairwise item similarity, and then propagates collaborative information from warm items to similar cold ones, i.e. to items that have no click data. The important feature of the proposed approach is its efficiency: it computes pairwise similarity in an implicit way and hence can deal with large-scale item catalogs. Warmth Propagation is not a stand-alone approach and can be applied to pretrained recommenders that were not originally designed to handle cold items, requiring only that item embeddings be initialized from content features. We experimentally show that the proposed approach introduces minimal computational overhead and outperforms various cold-start recommenders.