IEEE Trans Pattern Anal Mach Intell. 2017 Aug;39(8):1633-1647. doi: 10.1109/TPAMI.2016.2605085. Epub 2016 Sep 1.
Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of collaborative filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social collaborative filtering based on trust.
推荐系统用于准确、主动地为用户提供潜在有趣的信息或服务。协同过滤是一种广泛采用的推荐方法，但稀疏数据和冷启动用户通常是提供高质量推荐的障碍。为了解决这些问题，我们提出了一种新的方法，通过整合用户提供的稀疏评分数据和用户之间稀疏的社交信任网络，来提高协同过滤推荐的性能。这是一种基于模型的方法，采用矩阵分解技术，根据用户的信任关系将用户映射到低维潜在特征空间，旨在更准确地反映用户对自己观点形成的相互影响，并学习更好的用户优惠模式，以提供高质量的推荐。我们使用四个大规模数据集表明，与基于信任的社交协同过滤的最新推荐算法相比，所提出的方法的性能要好得多，尤其是对于冷启动用户。
