In recent years, we have witnessed a flourish of social commerce services. Online users can easily share their experiences on products or services with friends. Social recommender systems are employed to tailor right products for user needs. However, existing recommendation methods try to consider the social information to improve the recommendation performance while they do not differ the impact of different social information and do not have deep analysis on social information. In this paper, we propose a social recommendation framework to leverage the friend and group information to extend the traditional BPR model from different perspectives. Through a detailed experiment on LAST.FM data set, we find that the proposed methods are effective in improving the recommendation accuracy and we also have a good understanding for the impact of friend and group information on recommendation performance.

中文翻译：

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利用朋友和团体信息来改善社交推荐系统

近年来，我们目睹了社交商务服务的蓬勃发展。在线用户可以轻松地与朋友分享他们在产品或服务上的体验。社交推荐系统用于定制适合用户需求的产品。然而，现有的推荐方法试图考虑社交信息以提高推荐性能，同时它们没有区别不同社交信息的影响并且没有对社交信息进行深入分析。在本文中，我们提出了一个社交推荐框架，以利用朋友和团体信息从不同的角度扩展传统的BPR模型。通过对LAST.FM数据集的详细实验，