Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends

In recent years, the use of trust-based recommendation systems to predict the scores of items not rated by users has attracted many researchers' interest. Accordingly, they create a trusted network of users, move in the trust graph, and search for the desired rank among the users by creating a Trust Walker and Random walk algorithm. Meanwhile, we face some challenges such as calculating the level of trust between users, the movement of Trust Walker using Random walk (random route selection), not discovering the desired rank, and as a result, the algorithm failure. In the present study, in order to solve the mentioned challenges, a trust-based recommender system is presented that predicts the ranks of items that the target user has not rated. In the first stage, a trusted network is developed based on the three criteria. In the next step, we define a Trust Walker to calculate the level of trust between users, and we apply the Biased Random Walk (BRW) algorithm to move it; the proposed method recommends it to the target user in the case of finding the desired rank of the item, and if that item does not exist in the defined trust network, it uses association rules to recognize items that are dependent on the item being searched and recommends them to the target user. The evaluation of this research has been performed on three datasets, and the obtained results indicate higher efficiency and more accuracy of the proposed method.

Automated summary

This study introduces a trust-based recommender system that addresses the challenges of predicting scores for items not rated by users, using Trust Walker and random walk algorithms, and further improves prediction accuracy and efficiency by incorporating the Biased Random Walk algorithm.