Numerous services issued from the emergence of web technologies drive research on how to provide users with trusted and credible services aligned with their needs. To tackle the service targeting problem, recommender systems have been developed. They are grouped into content-based approaches and collaborative filtering based approaches. Strongly focused on the target user profile, content-based methods are inaccurate when the target user profile is poor. To remedy this, collaborative filtering based methods exploit past experiences from many users. In the literature, they are organized into rating methods and ranking methods. In this paper, we propose a trusted collaborative filtering based approach that combines assets of both rating and ranking methods. Our proposal is built on an enhanced hybrid similarity measure and a novel trustworthiness score that is thereafter used to select trusted and relevant user profiles involved in the prediction process. By employing a customized ranking measure, our method improves the service ranking precision without affecting the rating prediction accuracy. Experiments are conducted on the WS-Dream dataset containing 339 users and real-world Quality of Service values related to 5825 web services. Compared to state-of-the-art collaborative filtering based methods, the obtained results show that our proposal offers the best trade-off in terms of rating prediction accuracy and ranking prediction accuracy.

网络技术的兴起带来了无数的服务，推动了对如何为用户提供符合其需求的可信赖且可信的服务的研究。为了解决服务定位问题，已经开发了推荐系统。它们分为基于内容的方法和基于协作过滤的方法。如果目标用户配置文件很差，则基于内容的方法将非常专注于目标用户配置文件。为了解决这个问题，基于协作过滤的方法利用了许多用户的过去经验。在文献中，它们被组织为评级方法和排名方法。在本文中，我们提出了一种基于可信协作过滤的方法，该方法结合了评级和排名方法的资产。我们的建议基于增强的混合相似性度量和新颖的可信赖度评分，此分数随后可用于选择预测过程中涉及的可信任和相关用户配置文件。通过采用定制的排名度量，我们的方法在不影响评级预测准确性的情况下提高了服务排名精度。在WS-Dream数据集上进行了实验，该数据集包含339个用户以及与5825个Web服务相关的真实服务质量值。与基于最新协作过滤的方法相比，所获得的结果表明，我们的建议在评级预测准确性和排名预测准确性方面提供了最佳折衷方案。我们的方法在不影响评级预测准确性的情况下提高了服务排名精度。在WS-Dream数据集上进行了实验，该数据集包含339个用户以及与5825个Web服务相关的真实服务质量值。与基于最新协作过滤的方法相比，所获得的结果表明，我们的建议在评级预测准确性和排名预测准确性方面提供了最佳折衷方案。我们的方法在不影响评级预测准确性的情况下提高了服务排名精度。在WS-Dream数据集上进行了实验，该数据集包含339个用户以及与5825个Web服务相关的真实服务质量值。与基于最新协作过滤的方法相比，所获得的结果表明，我们的建议在评级预测准确性和排名预测准确性方面提供了最佳折衷方案。