Trust relationships between user pairs play a vital role in making decisions for social network users. In reality, available explicit trust relations are often extremely sparse; therefore, inferring unknown trust relations attracts increasing attention in recent years. In this paper, a new approach originating from machine learning is proposed to predict trust relationships in social networks by exploring an improved k-nearest neighbor algorithm based on distance weight. Firstly, they extract three critical attributes from users’ personal profiles and interactive information; then, an improved algorithm is proposed; finally, comparative analysis between them is performed by using real-world dataset to evaluate their performance in trust prediction.