A recommender system for social networks using link Prediction, clustering and genetic algorithm

In the era of widespread social networks, finding meaningful and relevant connections has become a major challenge. Traditional recommender methods often face challenges in providing accurate and relevant recommendations and cannot fully reflect users’ real interests and connections. These methods are usually unable to provide personalized and efficient recommendations due to the lack of consideration of users’ communication patterns and profile characteristics. Accordingly, in this research, a new recommender method for social networks is presented that operates based on a combination of link prediction, clustering, and genetic algorithm. The proposed method is able to provide more accurate and relevant recommendations by simultaneously considering users’ communication patterns and their profile characteristics. Link prediction helps detect likely relationships between users, while clustering improves prediction ability by clustering users having similar features. Genetic algorithm is also used to determine the best values ​​of the model’s most significant parameters. Thus, the model is able to dynamically adjust to the data and provide the best performance in a number of dimensions such as precision, recall, and F-measure. Experimental findings revealed that the proposed method outperformed the conventional methods and achieved over 98% accuracy. As such, this hybrid technique, through the use of efficient clustering, parameter tuning, and proper link prediction, has been able to act as a good recommender system for social networks and fulfills the need to detect meaningful and valuable connections in a positive manner.