A dynamic bidirectional heuristic trust path search algorithm

Abstract

Online social networks greatly promote peoples' online interaction, where trust plays a crucial role. Trust prediction with trust path search is widely used to help users find the trusted friends and obtain valid information. However, the shortcomings of accuracy and time still exist in some famous algorithms. Therefore, the dynamic bidirectional heuristic search (DBHS) algorithm is proposed in this paper to find the reliable trust path by studying the heuristic search. First, the trust value and path length are comprehensively considered to find the most trusted user. Specially, it constrains the traversal depth based on the ‘small world’ theory and obtains the acceptable path set by using the relaxation coefficient λ to relax the depth of the shortest path. By this way, some longer path with the higher trust can be considered to improve the precision of algorithm. Then, an adjustment factor is designed based on the meet in the middle (MM) algorithm to assign search weights to two directions based on the size of the search tree expanded, so as to improve the problem of no priori when fixed parameters are used. Besides, the complexity of unidirectional trust path search can also be reduced by searching from two directions, which can reduce the depth and improve the efficiency of search. Finally, the predictive trust degree is outputted by the trust propagation function. Two public datasets are used to generate experimental results, which show that DBHS can quickly search and form reliable trust relationship, and it partly improves other algorithms.

Research on Path Planning Method based on Graph Search Algorithm

“…For example, the GBFS algorithm mentioned earlier uses a heuristic function to estimate the cost of the cheapest path. And A* path planning algorithm based on bidirectional search is another optimization method [11].…”

Evaluating Pile-Supported Embankment Considering the Soil Anisotropy Effect

“…Therefore, it is necessary to conduct extensive research on the anisotropic mechanical behavior of soil. Some scholars have simulated the stress distribution of soil through deep learning, artificial neural networks, and artificial intelligence algorithms [21][22][23][24][25][26][27][28][29]. Li and Goh [30] used an improved constitutive model to characterize the anisotropic behavior of strength and stiffness of cohesive soil resulting from the rotation of the principal stress directions in order to calculate the safety factor of an undrained clay slope.…”

Intelligent media computing technology and application for media convergence

“…The second article integrates recommendations and trust relationship into recommendations systems and proposes a new method based on dynamic bidirectional heuristic trust path search algorithm [2]. It studies the search problem of reliable trust path from the perspective of heuristic search.…”