Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

• Main Authors: Fu Jie Tey, Tin-Yu Wu, Chiao-Ling Lin, Jiann-Liang Chen
• Format: Article
• Language: English
• Published: SpringerOpen 2021-07-01
• Series: Journal of Big Data
• Subjects: Social network; Indirect relation; Cold-start
• Online Access: https://doi.org/10.1186/s40537-021-00484-0

Abstract 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.