Abstract: Social networks are linked by one or more particular kinds of connections, including web links, friends, family, and the sharing of ideas and money. Graph theory is used to investigate social relationships in social network analysis. The individuals within the networks are the vertices, and the connections among them are the edges. Between vertices, there can be a wide variety of edges. Due to the rise in Internet usage, online shopping, and social media usage in recent years, recommender systems have become more and more popular. Numerous websites have been successful in putting this recommender system into place. This thesis introduced an approach that uses the B-mine method to explore common patterns and enhance the accuracy of identifying influential nodes in social networks. In this method, two user similarity criteria—coverage and confidence—were used simultaneously to improve the recommender system. The behavior of previous users is analyzed, and recommendations are made to the current user based on friends' behavior and similarity, as well as on their interactions and preferences across different groups. According to the simulation results, the suggested approach performs satisfactorily, with accuracy and sensitivity of 89% and 76%, respectively.

Tingting Feng, Wenya Jin and Wei Li. “Friend Recommender System to Influence Friends on Social Networks Based on B-Mine Method”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150645

@article{Feng2024,
title = {Friend Recommender System to Influence Friends on Social Networks Based on B-Mine Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150645},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150645},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Tingting Feng and Wenya Jin and Wei Li}
}