Generating Recommendations via Trust-Aware Recommendation System by the Topological Impact of Users in Social Trust Networks

Abstract

There are a large number of current recommendation methods that have issues with cold starts and sparsity. In this study, these issues are addressed by proposing a novel trust-based recommendation method, and the proposed method uses trust information along with rating values to deal with “cold-start” users and items. Because in most real-world applications, only a few items are given feedback by the users. Therefore, we were faced with a sparse user-item matrix. Here, similar users are grouped using a random-walk-based method that calculates the influence of users in social networks. Then cluster seeds are identified among the most influential users. Assign unique labels to cluster seeds and use a novel label propagation method to spread labels to unassigned users. Finally, the combinations identified in the prediction process are used to predict missing ratings. To assess the efficiency of the proposed approach, several experiments were performed on the well-known and widely used real-world dataset called FilmTrust. The results are compared based on several known evaluation metrics, which are F1-Measure, Precision, Recall, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The proposed method achieved the lowest values of MAE and RMSE and the highest values of F1, Precision, and Recall in comparison to the other recommended methods. Results showed that the proposed method is superior to the traditional and modern methods in terms of accuracy and efficiency in most cases. Therefore, it can be concluded that using trust information leads to more accurate rating predictions.