A cold-start recommendation method for We-map considering co-occurrence collaborative signals and contrastive learning

Abstract

ABSTRACT We-maps, augmenting traditional maps, confront cold-start problems with prevailing recommendation techniques. The Contrastive Collaborative Filtering (CCF) model tackles We-map's cold-start issues by harnessing co-occurrence signals from warm data in a contrastive framework, thereby refining embeddings through the integration of content and co-occurrence insights. This methodology engenders unique embeddings for We-map entities, utilizing contrastive learning to bridge semantic gaps and enable indirect knowledge transfer, effectively mitigating cold-start challenges. Experimental validation on We-map and various public datasets confirms the method's practicality in addressing cold-start recommendations for We-maps. The study offers a new path for efficient geospatial data dissemination.