论文标题
信任感知推荐系统的调查：深度学习观点
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
论文作者
论文摘要
对于现有推荐系统的一个重大挑战是，用户可能不信任缺乏解释或不准确建议结果的推荐系统。因此，接受值得信赖的推荐系统变得至关重要。这项调查提供了三类信任意识推荐系统的系统摘要：有利用用户社会关系的社会意识推荐系统；强大的推荐系统，这些系统会过滤不正确的噪声（例如，垃圾邮件发送者和虚假信息）或增强攻击性；可解释的推荐系统，提供推荐项目的解释。我们专注于基于深度学习技术的工作，这是推荐研究中的新兴领域。
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. We focus on the work based on deep learning techniques, an emerging area in the recommendation research.
