پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 677 |
| تعداد مقالات | 9,872 |
| تعداد مشاهده مقاله | 70,004,817 |
| تعداد دریافت فایل اصل مقاله | 49,283,942 |
A hybrid cumulative knowledge framework for friend recommendation in social networks | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 26 فروردین 1404 اصل مقاله (960.06 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.34433.5144 | ||
| نویسندگان | ||
| Mahdi Bazargani* 1؛ Nafiseh Fareghzadeh2؛ Mehdi Afzali1؛ Shiva Karimi1 | ||
| 1Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran | ||
| 2Department of Computer Engineering, Khodabandeh Branch, Islamic Azad University, Zanjan, Iran | ||
| تاریخ دریافت: 24 خرداد 1403، تاریخ پذیرش: 19 بهمن 1403 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Friend Recommender؛ Hybrid Framework؛ Social Networks؛ Cumulative Learning؛ New Knowledge | ||
| مراجع | ||
|
[1] M. Bazargani and S.H. Alizadeh, A deep neural network-based approach in tag recommender system to overcome users’ Cold Start, Int. J. Nonlinear Anal. Appl. 15 (2024), no. 7, 197–214. [2] M. Bazargani and E. Namazi, A study on a combined model in business intelligence for improving electronic insurance, Int. J. Bus. Intell. Res. 6 (2015), no. 1, 49–55. [3] L. Berkani, S. Belkacem, M. Ouafi, and A. Guessoum, Recommendation of users in social networks: A semantic and social based classification approach, Expert Syst. 38, no. 2 (2021), e12634. [4] L. Chen, M. Gao, B. Li, W. Liu, and B. Chen, Detect potential relations by link prediction in multi-relational social networks, Decision Support Syst. 115 (2018), 78–91. [5] Y. Deldjoo, T. D. Noia, and F. A. Merra, A survey on adversarial recommender systems: From attack/defense strategies to generative adversarial networks, ACM Comput. Surv. 54 (2021), no. 2, 1–38. [6] E. Kozegar, H. Yarmohammadi, M. Bazargani, and Z. Homayounpour, Presenting a novel method based on collaborative filtering for nearest neighbor detection in recommender systems, Intell. Multimed. Process. Commun. Syst. 1 (2020), no. 1, 55–64. [7] N. Li, Q. Huang, X. Ge, M. He, S. Cui, P. Huang, S. Li, and S.F. Fung, A review of the research progress of social network structure, Complexity 2021 (2021), no. 1, 6692210. [8] Z. Li, F. Xiong, X. Wang, H. Chen, and X. Xiong, Topological influence-aware recommendation on social networks, Complexity 2019 (2019), no. 1, 6325654.[9] S. Mallek, I. Boukhris, Z. Elouedi, and E. Lef`evre, Evidential link prediction in social networks based on structural and social information, J. Comput. Sci. 30 (2019), 98–107. [10] K. Madadipouya and S. Chelliah, A literature review on recommender systems algorithms, techniques and evalu[1]ations, Broad Res. Artific. Intell. Neurosci. 8 (2017), no. 2, 109–124. [11] I. Mashal, O. Alsaryrah, T.Y. Chung, and F.C. Yuan, A multi-criteria analysis for an internet of things application recommendation system, Technol. Soc. 60 (2020), 101216. [12] N. Nassar, A. Jafar, and Y. Rahhal, A novel deep multi-criteria collaborative filtering model for recommendation system, Knowledge-Based Syst. 187 (2020), 104811. [13] A. Papadimitriou, P. Symeonidis, and Y. Manolopoulos, Friendlink: link prediction in social networks via bounded local path traversal, Int. Conf. Comput. Aspects Soc. Networks, 2011, 66–71. [14] V.S. Parvathy and T. K. Ratheesh, Friend recommendation system for online social networks: A survey, Int. Conf. Electronics Commun. Aerospace Technol., Vol. 2, 2017, pp. 359–365. [15] J. Piao, G. Zhang, F. Xu, Z. Chen, Y. Zheng, C. Gao, and Y. Li, Bringing friends into the loop of recommender systems: An exploratory study, Proc. ACM Human-Comput. Interact., Vol. 5, 2021, pp. 1–26. [16] S.H. Shalforoushan and M. Jalali, Link prediction in social networks using Bayesian networks, Int. Symp. Artific. Intell. Signal Process., 2015, pp. 246–250. [17] P.K. Sharma, S. Rathore, and J.H. Park, Multilevel learning based modeling for link prediction and users’ consumption preference in Online Social Networks, Future Gen. Comput. Syst. 93 (2019), 952–961. [18] W. Yuan, K. He, D. Guan, L. Zhou, and C. Li, Graph kernel based link prediction for signed social networks, Inf. Fusion 46 (2019), 1-10. [19] S.Y. Wu, Q. Zhang, C.Y. Xue, and X.Y. Liao, Cold-start link prediction in multi-relational networks based on network dependence analysis, Phys. A: Statist. Mech. Appl. 515 (2019), 558–565. [20] H. Yuan and A.A. Hernandez, User cold start problem in recommendation systems: A systematic review, IEEE Access 11 (2023), 136958–136977. [21] J. Yuan, W. Wu, Y. Li, and D. Du, Active friending in online social networks, Proc. Fourth IEEE/ACM Int. Conf. Big Data Comput. Appl. Technol., 2017, 139–148. [22] H. Zarzour, F. Maazouzi, M. Al-Zinati, A. Nusayr, M. Alsmirat, M. Al-Ayyoub, and Y. Jararweh, Using K-means clustering ensemble to improve the performance in recommender systems, Int. Conf. Intell. Data Sci. Technol. Appl., 2022, pp. 176–180. [23] Z. Zhang, J. Wen, L. Sun, Q. Deng, S. Su, and P. Yao, Efficient incremental dynamic link prediction algorithms in social network, Knowledge-Based Syst. 132 (2017), 226–235. [24] H. Zou, Z. Gong, N. Zhang, and W. Zhao, TrustRank: A cold-start tolerant recommender system, Enterprise Inf. Syst. 9 (2015), no. 2, 117–138. | ||
|
آمار تعداد مشاهده مقاله: 489 تعداد دریافت فایل اصل مقاله: 242 |
||