Friendship Recommendation Algorithms Based on Machine Learning: A Review
Keywords:
Recommender systems, Friendship recommendation, Neural collaborative filteringAbstract
In today’s digital world, where online social networks are booming, recommender systems play a crucial role in managing the overwhelming amount of information. This paper focuses on friendship recommendation algorithms and their impact on facilitating social connections within the realm of online platforms. It starts by underscoring the importance of recommender systems in alleviating the information overload problem, then delves into an exploration of recommendation algorithms, specifically friending algorithms. The primary focus of the paper revolves around the integration of machine learning techniques into friendship recommendation algorithms, showcasing the potential of artificial intelligence in enhancing social interactions. By combining current research with practical insights, this paper highlights the harmony between machine learning and friendship recommendation algorithms, with the aim of improving personalized and rewarding social experiences in the digital landscape.
Downloads
References
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. Graph neural networks in recommender systems: a survey. ACM Computing Surveys, 55(5):1–37, 2022.
Huawei Yi, Jingtong Liu, Wenqian Xu, Xiaohui Li, and Huihui Qian. A graph neural network social recommendation algorithm integrating the multi-head attention mechanism. Electronics, 12(6):1477, 2023.
Hmood Al-Dossari, Fawaz Abu Nughaymish, Ziyad Al-Qahtani, Mohammed Alkahlifah, and Asma Alqahtani. A machine learning approach to career path choice for information technology graduates. Engineering, technology & applied science research, 10(6):6589–6596, 2020.
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 34(8):3549–3568, 2020.
Qian Zhang, Jie Lu, and Yaochu Jin. Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1):439–457, 2021.
Jiliang Tang, Xia Hu, and Huan Liu. Social recommendation: a review. Social Network Analysis and Mining, 3:1113–1133, 2013.
Vinita Rohilla, Mandeep Kaur, and Sudeshna Chakraborty. An empirical framework for recommendation-based location services using deep learning. Engineering, Technology & Applied Science Research, 12(5):9186–9191, 2022.
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining, pages 2221–2231, 2019.
Janneth Chicaiza and Priscila Valdiviezo-Diaz. A comprehensive survey of knowledge graph-based recommender systems: Technologies, development, and contributions. Information, 12(6):232, 2021.
Christian Desrosiers and George Karypis. A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook, pages 107–144, 2010.
Xiaoyuan Su and Taghi M Khoshgoftaar. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 2009.
Bo Jiang, Junchen Yang, Yanbin Qin, Tian Wang, Muchou Wang, and Weifeng Pan. A service recommendation algorithm based on knowledge graph and collaborative filtering. IEEE Access, 9:50880–50892, 2021.
Salony Mewara, Sneha Goyal, et al. A friend recommendation system using semantic based knn algorithm. arXiv preprint arXiv:2109.14970, 2021.
Liangwei Yang, Zhiwei Liu, Yingtong Dou, Jing Ma, and Philip S Yu. Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation. In Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval, pages 2141–2145, 2021.
Liang Chen, Yuanzhen Xie, Zibin Zheng, Huayou Zheng, and Jingdun Xie. Friend recommendation based on multi-social graph convolutional network. IEEE Access, 8:43618–43629, 2020.
Mahesh Thyluru Ramakrishna, Vinoth Kumar Venkatesan, Rajat Bhardwaj, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Saima Anwar Lashari, and Aliaa M Alabdali. Hcof: Hybrid collaborative filtering using social and semantic suggestions for friend recommendation. Electronics, 12(6):1365, 2023.
Aadil Alshammari and Abdelmounaam Rezgui. Cidf: A clustering-based interaction-driven friending algorithm for the next-generation social networks. IEEE Access, 7:153555–153565, 2019.
Aadil Alshammari and Abdelmounaam Rezgui. Better edges not bigger graphs: An interactiondriven friendship recommender algorithm for social networks. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), pages 1–8. IEEE, 2020.
Jibing Gong, Yi Zhao, Shuai Chen, Hongfei Wang, Linfeng Du, Shuli Wang, Md Zakirul Alam Bhuiyan, Hao Peng, and Bowen Du. Hybrid deep neural networks for friend recommendations in edge computing environment. IEEE Access, 8:10693–10706, 2019.
Daizong Ding, Mi Zhang, Shao-Yuan Li, Jie Tang, Xiaotie Chen, and Zhi-Hua Zhou. Baydnn: Friend recommendation with bayesian personalized ranking deep neural network. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 1479–1488, 2017.
Shaohua Tao, Runhe Qiu, Yuan Ping, Woping Xu, and Hui Ma. Making explainable friend recommendations based on concept similarity measurements via a knowledge graph. IEEE Access, 8:146027–146038, 2020.
Zhouhang Xie, Sameer Singh, Julian McAuley, and Bodhisattwa Prasad Majumder. Factual and informative review generation for explainable recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 13816–13824, 2023.
Chunying Li, Bingyang Zhou, Weijie Lin, Zhikang Tang, Yong Tang, Yanchun Zhang, and Jinli Cao. A personalized explainable learner implicit friend recommendation method. Data Science and Engineering, 8(1):23–35, 2023.
Shulin Cheng, Bofeng Zhang, Guobing Zou, Mingqing Huang, and Zhu Zhang. Friend recommendation in social networks based on multi-source information fusion. International Journal of Machine Learning and Cybernetics, 10:1003–1024, 2019.
Xiran Song, Jianxun Lian, Hong Huang, Mingqi Wu, Hai Jin, and Xing Xie. Friend recommendations with self-rescaling graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3909–3919, 2022.
Aadil Alshammari and Mohammed Alshammari. A recommendation engine model for giant social media platforms using a probabilistic approach. Engineering, Technology & Applied Science Research, 13(5):11904–11910, 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
