A Trust Prediction Method Based on Heterogeneous Information Networks

It is essential to predict the level of trust among users before they interact to reduce the risk of interaction. Due to the sparsity of trust relationships, it is inefficient to simply use explicit trust relationships to predict the trust among users, and even the trust path may be lost. On the other hand, there are implicit trust relationships among users such as the joint items that several users all rated. Once the trust relationship is extracted, it will greatly expand the number of trusted users. To this end, a trust prediction method incorporating rating information is proposed to address this problem. It first constructs a heterogeneous information network consisting of social and rating information. Secondly, in the trust prediction period, if the user has no trusted users to choose from, the joint item is used as a bridge to find implicit trusted users from users who have jointly rated the item. Finally, the Dueling DQN algorithm is used to calculate the strength of the trust path, and the predicted trust value is derived by aggregating multiple trust paths based on an aggregation function. The experimental results on two datasets indicate the presented approach outperforms most existing trust prediction methods.