International Journal of Nonlinear Analysis and Applications

A hybrid cumulative knowledge framework for friend recommendation in social networks

Articles in Press

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran

2 Department of Computer Engineering, Khodabandeh Branch, Islamic Azad University, Zanjan, Iran

Abstract

Recently, recommender systems have been a vital part of social network applications to help users make the best choices and expand their heuristic experiences. Friend recommender is a popular service that has been widely developed and focuses mainly on relationships and user interests. Traditional friend recommender systems suffer from some shortcomings that hamper their effectiveness. Hybrid techniques to recommender systems can significantly improve the overall quality of related applications. In this research, a hybrid cumulative knowledge framework (HCKF) has been designed for friend recommendation in social networks. HCKF performs a huge amount of precise evaluations on data, including pre-processing on the data, data preparation, optimal features selection and user clustering in the background; therefore, friend suggestions can be provided at an acceptable performance, precision and speed. HCKF solves the cold start challenge of new users, increases the accuracy of providing attractive friends to users and also minimizes the error rate. The experimental results demonstrate the advantages and effectiveness of the proposed framework in social networks. After simulating HCKF and calculating the error rate and accuracy, the final precision rate of the friend suggestion to the newly logged-in user is equal to 90\%. Moreover, the average accuracy in the proposed framework is equal to 0.0197, which has improved significantly compared to other related methods.

Keywords

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International Journal of Nonlinear Analysis and Applications

Articles in Press, Corrected Proof
Available Online from 15 April 2025
  • Receive Date: 13 June 2024
  • Accept Date: 07 February 2025