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. 

近年来，使用基于信任的推荐系统来预测用户未评分的项目的分数引起了许多研究人员的兴趣。因此，他们创建了一个可信的用户网络，在信任图中移动，并通过创建 Trust Walker 和 Random walk 算法在用户中搜索所需的排名。同时，我们面临一些挑战，例如计算用户之间的信任程度，Trust Walker 使用 Random walk（随机路由选择）的移动，没有发现所需的等级，从而导致算法失败。在本研究中，为了解决上述挑战，提出了一种基于信任的推荐系统，该系统可以预测目标用户尚未评级的项目的排名。在第一阶段，基于三个标准开发可信网络。在下一步中，我们定义了一个 Trust Walker 来计算用户之间的信任程度，我们应用偏置随机游走 (BRW) 算法来移动它；所提出的方法在找到项目所需等级的情况下将其推荐给目标用户，如果该项目在定义的信任网络中不存在，则它使用关联规则来识别依赖于正在搜索的项目的项目和将它们推荐给目标用户。本研究在三个数据集上进行了评估，获得的结果表明所提出的方法具有更高的效率和更高的准确性。如果该项目在定义的信任网络中不存在，则它使用关联规则识别依赖于正在搜索的项目的项目并将其推荐给目标用户。本研究在三个数据集上进行了评估，获得的结果表明所提出的方法具有更高的效率和更高的准确性。如果该项目在定义的信任网络中不存在，则它使用关联规则识别依赖于正在搜索的项目的项目并将其推荐给目标用户。本研究在三个数据集上进行了评估，获得的结果表明所提出的方法具有更高的效率和更高的准确性。