Recent advances on Internet applications have facilitated information spreading. Thanks to a wide variety of mobile devices and the burgeoning 5G networks, users gain access easily and quickly to information. Also, the great amount of digital information has contributed to the emergence of recommender systems that help information filtering. As the rise of mobile networks has pushed forward the growth of social media networks, users have gotten used to posting whatever they do and wherever they visit on the Web. Nevertheless, quick social media updates can 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. There is normally no corresponding reference value for new products or services, so we use the indirect relations between friends and "friends’ friends" as well as sentinel friends to improve the recommendation accuracy. Our proposed mechanism has been proven efficient in enhancing recommendation accuracy.