Trust management has become an emerging security paradigm in various areas such as ad hoc networks and cloud computing. One core element of trust management is the indirect trust model that evaluates the trustworthiness of a target based on others' recommendations. The research on indirect trust is still at an early stage, and some problems are not addressed yet. Because of the subjectivity of trust, entities would have different views on a same target. Consequently, after receiving recommendations, the evaluating entity should first measure their credibility. Existing methods often distort recommendations. We propose a more reasonable method based on the similarity between recommenders and evaluating entities. Furthermore, considering the characteristics of one-hop and multi-hop recommendations, the similarity calculation methods for them were developed individually. Another problem is the spoofed recommendations aiming at tarnishing someone or harboring conspirators. We design a simple but efficient algorithm to detect and remove them. The proposed methods and algorithms constitute the integrated indirect trust model. This model is not bound to any specific domain, and thus it can be widely applied. Simulation results show that the model is effective in obtaining an objective indirect trust value with the existence of personalized and dishonest recommenders. Copyright (C) 2017 John Wiley & Sons, Ltd.