  The use of recommender systems has increased dramatically to assist online
social network users in the decision-making process and selecting appropriate
items. On the other hand, due to many different items, users cannot score a
wide range of them, and usually, there is a scattering problem for the matrix
created for users. To solve the problem, the trust-based recommender systems
are applied to predict the score of the desired item for the user. Various
criteria have been considered to define trust, and the degree of trust between
users is usually calculated based on these criteria. In this regard, it is
impossible to obtain the degree of trust for all users because of the large
number of them in social networks. Also, for this problem, researchers use
different modes of the Random Walk algorithm to randomly visit some users,
study their behavior, and gain the degree of trust between them. In the present
study, a trust-based recommender system is presented that predicts the score of
items that the target user has not rated, and if the item is not found, it
offers the user the items dependent on that item that are also part of the
user's interests. In a trusted network, by weighting the edges between the
nodes, the degree of trust is determined, and a TrustWalker is developed, which
uses the Biased Random Walk (BRW) algorithm to move between the nodes. The
weight of the edges is effective in the selection of random steps. The
implementation and evaluation of the present research method have been carried
out on three datasets named Epinions, Flixster, and FilmTrust; the results
reveal the high efficiency of the proposed method.
