Exploring indirect entity relations for knowledge graph enhanced recommender system

ntity elation imilarity and ndirect eedback-based nowledge graph enhanced ecommendation (ERSIF-KR) to enhance representation learning in KG-based recommender systems. In addition, our model exploits indirect feedback of items that are not directly interacted with users to alleviate the exposure bias while enhancing user similarity computation when learning user representation. Moreover, our method directly incorporates representation of multi-hop neighbors into the target item embedding with weights determined by the correlations between high-order and low-order relations, which can significantly boost the item representation learning. Extensive experiments on three real-world datasets demonstrate that our model achieves remarkable gains in terms of recommendation performance and model convergence time, and effectively alleviates the sparsity and cold start problems.