Social Collaborative Filtering by Trust
Social Collaborative Filtering by Trust
复制标题
通过信任进行社交协同过滤
DOI：
10.1109/tpami.2016.2605085
复制
发表时间：
2017-08-01
影响因子：
23.6
通讯作者：
Li, Wenjie
中科院分区：
文献类型：
--
作者：
Yang, Bo;Lei, Yu;Li, Wenjie
推荐系统用于准确和主动地向用户提供潜在的感兴趣的信息或服务。协同过滤是一种被广泛采用的推荐方法，但稀疏数据和冷启动用户往往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，工程，以提高协同过滤推荐的性能，通过整合稀疏的评级数据的用户和稀疏的社会信任网络之间的这些相同的用户。这是一种基于模型的方法，采用矩阵分解技术，将用户映射到低维的潜在特征空间，根据他们的信任关系，旨在更准确地反映用户的相互影响，形成自己的意见，并学习更好的用户的偏好模式，高质量的推荐。我们使用了四个大规模的数据集，以表明该方法的性能更好，特别是冷启动用户，比国家的最先进的推荐算法，基于信任的社会协同过滤。
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.