In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers’ demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. In this paper, we propose a novel graph-based recommendation model to achieve personalized item ranking. To be specific, we design an adapted semi-supervised learning method to capture item smoothness, item fitting, and item confidence. By exploiting the structure of item graph moderately, the proposed method achieves impressive effectiveness and efficiency. In addition, extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the-art counterparts on the top-N recommendation task.