Dynamic Weighted Heuristic Trust Path Search Algorithm

Ru Kong,Xiangrong Tong

doi:10.1109/access.2020.3019797

Abstract

Trust propagation is being increasingly adopted to assist recommendation systems in providing more reliable information, upon which, users can make more accurate decisions. Optimal trust path search integrating trust value and path length plays a critical role in trust propagation, but suffers from insufficient performance regarding search accuracy and time. Generally, the quality of trust propagation is affected by the path length, and the longer the path is, the worse the trust quality is. However, the length of the path is not the unique crucial factor. Some longer paths with greater trust values may be more credible. In addition, the A* algorithm can find an optimal solution, but it expends much time to distinguish some similar paths. The A* algorithm is improved and a dynamic weighted heuristic trust path search algorithm is proposed. According to the six-degree space theory, the paths are extended to six-degree admissible trust paths. Then, according to the depths of the nodes in the search path, it relaxes the evaluation functionf(n) by devising a dynamic weighted factor w, inserts all nodes satisfied specific conditions into the FOCAL list. Furthermore, it sets the secondary heuristic factor and selects the nodes with the minimum heuristic factor value to reach the target node, and outputs the optimal trust path. Experiments on the public Advogato and FilmTrust datasets demonstrated that the proposed algorithm could efficiently identify the reliable trust paths and predict trust value with high accuracy and reduced computational complexity. The proposed algorithm could be applied to recommendation systems in the future.

Highlights

Trust recommendation systems are important applications based on social networks [1], [2], which combine the trust relationship between users to recommend items to users
4) METHODS FOR COMPARISON To demonstrate the accuracy of the DWHS, the comparison experiments are conducted with the WHST proposed by Wei and Tong [26], the classic TidalTrust proposed by Golbeck et al, the MoleTrust proposed by Massa et al, and the A∗ algorithm proposed by Hart
With the widespread application of recommendation technology in e-commerce systems, increasing attention has been devoted to the research on the precision and quality of trust recommendation systems

Summary

Introduction

Trust recommendation systems are important applications based on social networks [1], [2], which combine the trust relationship between users to recommend items to users. A. TRUST ALGORITHMS Trust is essential to reduce uncertainty and enhance collaboration in many practical applications, including social networks [10], [11] large-scale Internet of Things systems [12], [13], peer-to-peer networks [14], and wireless sensor networks [15]. TRUST ALGORITHMS Trust is essential to reduce uncertainty and enhance collaboration in many practical applications, including social networks [10], [11] large-scale Internet of Things systems [12], [13], peer-to-peer networks [14], and wireless sensor networks [15] In these applications, trust inference is widely used as a mechanism to establish trust between unknown users.

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Conclusion