Abstract The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

Nowadays, there are many Web services with similar functionality on the Internet. Users consider Quality of Service (QoS) of the services to select the best service from among them. The prediction of QoS values of the Web services and recommendations of the best service based on these values to the users is one of the major challenges in the web service area. Major studies in this field use collaboration filtering based methods for prediction. The paper introduced prediction methods and divided them into three main categories: memory-based methods, model-based methods, and Collaborative Filtering (CF) methods combined with other methods. In each category, some of the most famous studies were introduced, and then the problems and benefits of each category were reviewed. Finally, we have a discussion about these methods and propose suggestions for future works.

Abstract Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.