Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

互联网应用的最新进展促进了信息传播，并且由于种类繁多的移动设备和蓬勃发展的 5G 网络，用户可以轻松快速地获取信息。此外，大量数字信息促成了有助于过滤信息的推荐系统的出现。当移动网络的兴起推动社交媒体网络的发展，用户习惯于在网络上发布他们所做的任何事情以及他们访问的任何地方时，这种快速的社交媒体更新已经使用户很难找到历史数据。为此，本文提出了一种基于社交网络的推荐系统。我们的目的是建立一个以用户为中心的推荐系统，根据用户的喜好和朋友的喜好排除用户不感兴趣的产品。购物体验，从而使推荐有效。由于新产品或新服务可能没有相应的参考价值，我们使用朋友与“朋友的朋友”以及哨兵朋友之间的间接关系来提高推荐准确性。仿真结果证明了我们提出的机制在提高推荐精度方面是有效的。