In this paper, we aim at developing a new collaborative filtering recommender system using soft ratings, which is capable of dealing with both imperfect information about user preferences and the sparsity problem. On the one hand, Dempster–Shafer theory is employed for handling the imperfect information due to its advantage in providing not only a flexible framework for modeling uncertain, imprecise, and incomplete information, but also powerful operations for fusion of information from multiple sources. On the other hand, in dealing with the sparsity problem, community context information that is extracted from the social network containing all users is used for predicting unprovided ratings. As predicted ratings are not a hundred percent accurate, while the provided ratings are actually evaluated by users, we also develop a new method for calculating user–user similarities, in which provided ratings are considered to be more significant than predicted ones. In the experiments, the developed recommender system is tested on two different data sets; and the experiment results indicate that this system is more effective than CoFiDS, a typical recommender system offering soft ratings.

中文翻译：

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使用软评级将社区上下文信息集成到一个可靠加权的协同过滤系统中

在本文中，我们的目标是开发一种新的使用软评级的协同过滤推荐系统，该系统能够处理关于用户偏好的不完美信息和稀疏问题。一方面，Dempster-Shafer 理论被用于处理不完美信息，因为它不仅为不确定、不精确和不完整信息的建模提供了一个灵活的框架，而且还提供了对来自多个来源的信息进行融合的强大操作。另一方面，在处理稀疏性问题时，从包含所有用户的社交网络中提取的社区上下文信息用于预测未提供的评分。由于预测的评分不是百分百准确，而提供的评分实际上是由用户评估的，我们还开发了一种计算用户-用户相似度的新方法，其中提供的评分被认为比预测的评分更重要。在实验中，开发的推荐系统在两个不同的数据集上进行了测试；实验结果表明，该系统比 CoFiDS 更有效，CoFiDS 是一种典型的提供软评级的推荐系统。