Abstract
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 (WKNN). Firstly, we extract three critical attributes from users' personal profiles and interactive information; then, an improved KNN algorithm named WKNN is proposed; finally, comparative analysis between them is performed by using real-world dataset from Epinions to evaluate their performance in trust prediction. Empirical evaluation demonstrates that the proposed framework (WKNN model) is feasible and effective in predicting trust relationships.
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