Abstract
Recommender Systems (RS) aim to predict users' latent interests in items by learning embeddings from user-item graphs. Graph Neural Networks (GNNs) have significantly advanced RS by enabling the embedding of graph-structured data. However, relying solely on user-item interactions has limitations, such as the cold-start problem. Social recommendation has gained attention for its potential to improve outcomes by incorporating social information among users. Yet, existing social-aware models need further exploration of interaction semantics and other collaborative relationships beyond social connections. This paper addresses these limitations by proposing CoHet4Rec, a recommendation model leveraging GNNs and a Collaborative Heterogeneous Information Network (CHIN) with latent collaborative heterogeneous relation factors. CoHet4Rec captures diverse connections between users and items through factorized representations, and has the flexibility to easily incorporate more knowledge beyond social networks to alleviate data sparsity and cold-start problem. Extensive experiments on three benchmark datasets demonstrate the superiority of CoHet4Rec over 15 state-of-the-art (SOTA) recommendation techniques. The highest average improvement is 31.88% for HR@5 and 38.39% for NDCG@5.