نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد رشته فناوری اطلاعات، دانشگاه قم، قم، ایران
2 استادیار مهندسی کامپیوتر، دانشگاه قم، قم، ایران نویسنده مسئول: jalaly@qom.ac.ir
چکیده
در سال‌های اخیر تعداد کاربران شبکه‌های اجتماعی رشد زیادی داشته‌اند. چالش بزرگ مخاطب این شبکه‌ها، نحوه برقراری ارتباط با افراد حاضر در این شبکه‌ها می‌باشد. سیستم‌های پیشنهاددهنده دوست با ارائه پیشنهاداتی سعی در رفع این چالش دارند. در این پژوهش از داده‌‌های شبکه علمی و اجتماعی کوثرنت استفاده شده است. در این تحقیق با استفاده از 10 نوع رابطه بین کاربران و بدون در نظر گرفتن روابط دوستی،گراف شبکه ایجاد و سپس با استفاده از 3 الگوریتم لووین[1]، کی‌میانگین[2] و سلسله‌مراتبی[3]، خوشه‌بندی گراف جهت تشخیص جوامع انجام گردید. خوشه‌های به دست آمده از الگوریتم خوشه‌بندی لووین دارای درصد مطابقت بالاتری با روابط دوستی بودند. سپس با استفاده از الگوریتم ژنتیک[4] برای هر یک از 10 رابطه وزن‌های مختلفی در نظر گرفته شد و با اجرای الگوریتم خوشه‌بندی لووین بر روی گراف شبکه، بیشترین درصد مطابقت به همراه وزن بهینه هر یک از 10 رابطه به دست آمد. در این حالت خوشه‌های حاصل، خوشه‌هایی بهینه حاوی کاربران با بیشترین شباهت هستند. بنابراین می‌توان سایر کاربرانی که در یک خوشه قرار گرفته‌اند به عنوان دوست به یکدیگر پیشنهاد داد. برای اولویت‌بندی پیشنهادات نیز از وزن یال‌های بین افراد در گراف استفاده شد. در پایان روش پیشنهاد دوست ارزیابی و درصد مطابقت دوستان پیشنهادی با دوستان واقعی فرد محاسبه گردید.
[1]. Louvain
[2]. Kmeans
[3]. Hierarchical
[4] .Genetic
کلیدواژه‌ها
عنوان مقاله [English]
Synergy Creation Of Users In Kousarnet Social Scientific Network Using Graph Based Clustering Methods
نویسندگان [English]
- Zahra Shirani 1
- Amir Jalaly Bidgoly 2
1 Department of Information and Computer Engineering, University Of Qom, Qom Iran.
2 Department of Information and Computer Engineering, University Of Qom, Qom.Iran.Corresponding Author: jalaly@qom.ac.ir
چکیده [English]
In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from the social and scientific network of Kousarent were used. In this research, using 10 types of relationships between users without considering the friendship relationships, network graph created, and then by using 3 algorithms Louvain, Kmeans and Hierarchical graph clustering was performed to identify communities. Clusters obtained from Louvain's clustering algorithm had higher percentages of matching with friendships. Then, weights were calculated by genetic algorithm for each of 10 relationships and by applying Louvain clustering algorithm on the network graph, the highest percentage of matching with the optimal weight of each of the 10 relationships was obtained. In this case, the resulting clusters are optimal clusters containing the most similar users. So other users in the same cluster can be suggested as friends. The weight of the edges between the individuals in the graph was also used to prioritize the bids. At the end, the friend proposed method was evaluated and the percentage of suggested friends matched with the individual's true friends was calculated.
کلیدواژه‌ها [English]
- Recommendation Systems
- Graph Clustering
- Community Detection
- Kousarnet Social Science Network
منابع
جلالی، امید. (1393). سیستم پیشنهاددهنده هم‌پژوهشی (مورد مطالعه: شبکه اجتماعی کوثرنت). پایان نامه کارشناسی ارشد رشته مهندسی فناوری اطلاعات گرایش تجارت الکترونیک، دانشکده فنی و مهندسی، دانشگاه قم.
Amigó, E., Gonzalo, J., Artiles, J., & Verdejo, F. (2009). A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4), 461-486.
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations inlocation-based social networks: a survey.GeoInformatica, 19(3), 525-565.
Bradnova, V., Chernyavsky, M. M., Just, L., Haiduc, M., Kharlamov, S. P., Kovalenko, A. D., & Zarubin, P. L. (2003). Nuclear Clustering Quest in Relativistic Multifragmentation. Paper presented at the Few-Body Problems in Physics ’02, Vienna: Springer Vienna, Vol. 14, 241-244.
Chen, J., Geyer, W., Dugan, C., Muller, M., & Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 201-210.
Chu, C.-H., Wu, W.-C., Wang, C.-C., Chen, T.-S., & Chen, J.-J. (2013). Friend Recommendation for Location-Based Mobile Social Networks. Paper presented at the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 365-370.
Ding, D., Zhang, M., Li, S.-Y., Tang, J., Chen, X., & Zhou, Z.-H. (2017). BayDNN : Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17, 1479-1488.
Du, Z., Hu, L., Fu, X., & Liu, Y. (2014). Scalable and Explainable Friend Recommendation in Campus Social Network System. Paper presented at the Frontier and Future Development of Information Technology in Medicine and Education, Dordrecht: Springer Netherlands, Vol. 269, 457-466.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern Recognition, 41(1), 176-190.
Hamid, M. N., Naser, M. A., Hasan, M. K., & Mahmud, H. (2014). A cohesion-based friend-recommendation system. Social Network Analysis and Mining, 4(1), 176-186.
Kherad, M., & Bidgoly, A. J. (2020). Recommendation system using a deep learning and graph analysis approach. arXiv preprint arXiv:2004.08100.
Li, S., Song, X., Lu, H., Zeng, L., Shi, M., & Liu, F. (2020). Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Systems with Applications, 139, 112839.
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.
Likas, A., Vlassis, N., & J. Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451-461.
Sammer A, Q., & Ayad R, A. (2019). Survey of User to User Recommendation System in Online Social Networks. Engineering and Technology Journal, 37(10), 422-428.
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 2(2), 169-194.
Suman Venkata, S., Yuvraj Singh, C., Swaraj, K.,& Tripathy, B. (2020). Recommendation System Using Community Identification. International Conference on Innovative Computing and Communications, Vol. 1087, 125-132.
Swarnakar, P., Kumar, A., & Tyagi, H. (2017). Network dynamics in friend recommendation: a study of Indian engineering students. Int. J. Information Technology and Management, 16(3), 287-300.
Wan, S., Lan, Y., Guo, J., Fan, C., & Cheng, X. (2013). Informational friend recommendation in social media. Paper presented at the Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, Dublin, Ireland, 1045-1048.
Wang, Z., Liao, J., Cao, Q., Qi, H., & Wang, Z. (2015). Friendbook: A Semantic-Based Friend Recommendation System for Social Networks. IEEE Transactions on Mobile Computing, 14(3), 538-551.
Weng, L., & Zhang, Q. (2020). A social recommendation method based on opinion leaders. Multimedia Tools and Applications, 1-16.
Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1-13.
Yu, Z., Wang, C., Bu, J., Wang, X., Wu, Y., & Chen, C. (2015). Friend recommendation with content spread enhancement in social networks. Information Sciences, 309, 102-118.
Zhao, X., Ma, Z., & Zhang, Z. (2017). A novel recommendation system in location-based social networks using distributed ELM. Memetic Computing, 10(3), 321-331.
Zheng, H., & Wu, J. (2017). Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization. Paper presented at the in Computer Communication and Networks (ICCCN), 2017 26th International Conference on IEEE, 1-9.
منابع
جلالی، امید. (1393). سیستم پیشنهاددهنده هم‌پژوهشی (مورد مطالعه: شبکه اجتماعی کوثرنت). پایان نامه کارشناسی ارشد رشته مهندسی فناوری اطلاعات گرایش تجارت الکترونیک، دانشکده فنی و مهندسی، دانشگاه قم.
Amigó, E., Gonzalo, J., Artiles, J., & Verdejo, F. (2009). A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4), 461-486.
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations inlocation-based social networks: a survey.GeoInformatica, 19(3), 525-565.
Bradnova, V., Chernyavsky, M. M., Just, L., Haiduc, M., Kharlamov, S. P., Kovalenko, A. D., & Zarubin, P. L. (2003). Nuclear Clustering Quest in Relativistic Multifragmentation. Paper presented at the Few-Body Problems in Physics ’02, Vienna: Springer Vienna, Vol. 14, 241-244.
Chen, J., Geyer, W., Dugan, C., Muller, M., & Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 201-210.
Chu, C.-H., Wu, W.-C., Wang, C.-C., Chen, T.-S., & Chen, J.-J. (2013). Friend Recommendation for Location-Based Mobile Social Networks. Paper presented at the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 365-370.
Ding, D., Zhang, M., Li, S.-Y., Tang, J., Chen, X., & Zhou, Z.-H. (2017). BayDNN : Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17, 1479-1488.
Du, Z., Hu, L., Fu, X., & Liu, Y. (2014). Scalable and Explainable Friend Recommendation in Campus Social Network System. Paper presented at the Frontier and Future Development of Information Technology in Medicine and Education, Dordrecht: Springer Netherlands, Vol. 269, 457-466.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern Recognition, 41(1), 176-190.
Hamid, M. N., Naser, M. A., Hasan, M. K., & Mahmud, H. (2014). A cohesion-based friend-recommendation system. Social Network Analysis and Mining, 4(1), 176-186.
Kherad, M., & Bidgoly, A. J. (2020). Recommendation system using a deep learning and graph analysis approach. arXiv preprint arXiv:2004.08100.
Li, S., Song, X., Lu, H., Zeng, L., Shi, M., & Liu, F. (2020). Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Systems with Applications, 139, 112839.
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.
Likas, A., Vlassis, N., & J. Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451-461.
Sammer A, Q., & Ayad R, A. (2019). Survey of User to User Recommendation System in Online Social Networks. Engineering and Technology Journal, 37(10), 422-428.
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 2(2), 169-194.
Suman Venkata, S., Yuvraj Singh, C., Swaraj, K.,& Tripathy, B. (2020). Recommendation System Using Community Identification. International Conference on Innovative Computing and Communications, Vol. 1087, 125-132.
Swarnakar, P., Kumar, A., & Tyagi, H. (2017). Network dynamics in friend recommendation: a study of Indian engineering students. Int. J. Information Technology and Management, 16(3), 287-300.
Wan, S., Lan, Y., Guo, J., Fan, C., & Cheng, X. (2013). Informational friend recommendation in social media. Paper presented at the Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, Dublin, Ireland, 1045-1048.
Wang, Z., Liao, J., Cao, Q., Qi, H., & Wang, Z. (2015). Friendbook: A Semantic-Based Friend Recommendation System for Social Networks. IEEE Transactions on Mobile Computing, 14(3), 538-551.
Weng, L., & Zhang, Q. (2020). A social recommendation method based on opinion leaders. Multimedia Tools and Applications, 1-16.
Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1-13.
Yu, Z., Wang, C., Bu, J., Wang, X., Wu, Y., & Chen, C. (2015). Friend recommendation with content spread enhancement in social networks. Information Sciences, 309, 102-118.
Zhao, X., Ma, Z., & Zhang, Z. (2017). A novel recommendation system in location-based social networks using distributed ELM. Memetic Computing, 10(3), 321-331.
Zheng, H., & Wu, J. (2017). Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization. Paper presented at the in Computer Communication and Networks (ICCCN), 2017 26th International Conference on IEEE, 1-9.
منابع
جلالی، امید. (1393). سیستم پیشنهاددهنده هم‌پژوهشی (مورد مطالعه: شبکه اجتماعی کوثرنت). پایان نامه کارشناسی ارشد رشته مهندسی فناوری اطلاعات گرایش تجارت الکترونیک، دانشکده فنی و مهندسی، دانشگاه قم.
Amigó, E., Gonzalo, J., Artiles, J., & Verdejo, F. (2009). A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4), 461-486.
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations inlocation-based social networks: a survey.GeoInformatica, 19(3), 525-565.
Bradnova, V., Chernyavsky, M. M., Just, L., Haiduc, M., Kharlamov, S. P., Kovalenko, A. D., & Zarubin, P. L. (2003). Nuclear Clustering Quest in Relativistic Multifragmentation. Paper presented at the Few-Body Problems in Physics ’02, Vienna: Springer Vienna, Vol. 14, 241-244.
Chen, J., Geyer, W., Dugan, C., Muller, M., & Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 201-210.
Chu, C.-H., Wu, W.-C., Wang, C.-C., Chen, T.-S., & Chen, J.-J. (2013). Friend Recommendation for Location-Based Mobile Social Networks. Paper presented at the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 365-370.
Ding, D., Zhang, M., Li, S.-Y., Tang, J., Chen, X., & Zhou, Z.-H. (2017). BayDNN : Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17, 1479-1488.
Du, Z., Hu, L., Fu, X., & Liu, Y. (2014). Scalable and Explainable Friend Recommendation in Campus Social Network System. Paper presented at the Frontier and Future Development of Information Technology in Medicine and Education, Dordrecht: Springer Netherlands, Vol. 269, 457-466.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern Recognition, 41(1), 176-190.
Hamid, M. N., Naser, M. A., Hasan, M. K., & Mahmud, H. (2014). A cohesion-based friend-recommendation system. Social Network Analysis and Mining, 4(1), 176-186.
Kherad, M., & Bidgoly, A. J. (2020). Recommendation system using a deep learning and graph analysis approach. arXiv preprint arXiv:2004.08100.
Li, S., Song, X., Lu, H., Zeng, L., Shi, M., & Liu, F. (2020). Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Systems with Applications, 139, 112839.
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.
Likas, A., Vlassis, N., & J. Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451-461.
Sammer A, Q., & Ayad R, A. (2019). Survey of User to User Recommendation System in Online Social Networks. Engineering and Technology Journal, 37(10), 422-428.
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 2(2), 169-194.
Suman Venkata, S., Yuvraj Singh, C., Swaraj, K.,& Tripathy, B. (2020). Recommendation System Using Community Identification. International Conference on Innovative Computing and Communications, Vol. 1087, 125-132.
Swarnakar, P., Kumar, A., & Tyagi, H. (2017). Network dynamics in friend recommendation: a study of Indian engineering students. Int. J. Information Technology and Management, 16(3), 287-300.
Wan, S., Lan, Y., Guo, J., Fan, C., & Cheng, X. (2013). Informational friend recommendation in social media. Paper presented at the Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, Dublin, Ireland, 1045-1048.
Wang, Z., Liao, J., Cao, Q., Qi, H., & Wang, Z. (2015). Friendbook: A Semantic-Based Friend Recommendation System for Social Networks. IEEE Transactions on Mobile Computing, 14(3), 538-551.
Weng, L., & Zhang, Q. (2020). A social recommendation method based on opinion leaders. Multimedia Tools and Applications, 1-16.
Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1-13.
Yu, Z., Wang, C., Bu, J., Wang, X., Wu, Y., & Chen, C. (2015). Friend recommendation with content spread enhancement in social networks. Information Sciences, 309, 102-118.
Zhao, X., Ma, Z., & Zhang, Z. (2017). A novel recommendation system in location-based social networks using distributed ELM. Memetic Computing, 10(3), 321-331.
Zheng, H., & Wu, J. (2017). Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization. Paper presented at the in Computer Communication and Networks (ICCCN), 2017 26th International Conference on IEEE, 1-9.
منابع
جلالی، امید. (1393). سیستم پیشنهاددهنده هم‌پژوهشی (مورد مطالعه: شبکه اجتماعی کوثرنت). پایان نامه کارشناسی ارشد رشته مهندسی فناوری اطلاعات گرایش تجارت الکترونیک، دانشکده فنی و مهندسی، دانشگاه قم.
Amigó, E., Gonzalo, J., Artiles, J., & Verdejo, F. (2009). A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4), 461-486.
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations inlocation-based social networks: a survey.GeoInformatica, 19(3), 525-565.
Bradnova, V., Chernyavsky, M. M., Just, L., Haiduc, M., Kharlamov, S. P., Kovalenko, A. D., & Zarubin, P. L. (2003). Nuclear Clustering Quest in Relativistic Multifragmentation. Paper presented at the Few-Body Problems in Physics ’02, Vienna: Springer Vienna, Vol. 14, 241-244.
Chen, J., Geyer, W., Dugan, C., Muller, M., & Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 201-210.
Chu, C.-H., Wu, W.-C., Wang, C.-C., Chen, T.-S., & Chen, J.-J. (2013). Friend Recommendation for Location-Based Mobile Social Networks. Paper presented at the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 365-370.
Ding, D., Zhang, M., Li, S.-Y., Tang, J., Chen, X., & Zhou, Z.-H. (2017). BayDNN : Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17, 1479-1488.
Du, Z., Hu, L., Fu, X., & Liu, Y. (2014). Scalable and Explainable Friend Recommendation in Campus Social Network System. Paper presented at the Frontier and Future Development of Information Technology in Medicine and Education, Dordrecht: Springer Netherlands, Vol. 269, 457-466.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern Recognition, 41(1), 176-190.
Hamid, M. N., Naser, M. A., Hasan, M. K., & Mahmud, H. (2014). A cohesion-based friend-recommendation system. Social Network Analysis and Mining, 4(1), 176-186.
Kherad, M., & Bidgoly, A. J. (2020). Recommendation system using a deep learning and graph analysis approach. arXiv preprint arXiv:2004.08100.
Li, S., Song, X., Lu, H., Zeng, L., Shi, M., & Liu, F. (2020). Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Systems with Applications, 139, 112839.
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.
Likas, A., Vlassis, N., & J. Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451-461.
Sammer A, Q., & Ayad R, A. (2019). Survey of User to User Recommendation System in Online Social Networks. Engineering and Technology Journal, 37(10), 422-428.
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 2(2), 169-194.
Suman Venkata, S., Yuvraj Singh, C., Swaraj, K.,& Tripathy, B. (2020). Recommendation System Using Community Identification. International Conference on Innovative Computing and Communications, Vol. 1087, 125-132.
Swarnakar, P., Kumar, A., & Tyagi, H. (2017). Network dynamics in friend recommendation: a study of Indian engineering students. Int. J. Information Technology and Management, 16(3), 287-300.
Wan, S., Lan, Y., Guo, J., Fan, C., & Cheng, X. (2013). Informational friend recommendation in social media. Paper presented at the Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, Dublin, Ireland, 1045-1048.
Wang, Z., Liao, J., Cao, Q., Qi, H., & Wang, Z. (2015). Friendbook: A Semantic-Based Friend Recommendation System for Social Networks. IEEE Transactions on Mobile Computing, 14(3), 538-551.
Weng, L., & Zhang, Q. (2020). A social recommendation method based on opinion leaders. Multimedia Tools and Applications, 1-16.
Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1-13.
Yu, Z., Wang, C., Bu, J., Wang, X., Wu, Y., & Chen, C. (2015). Friend recommendation with content spread enhancement in social networks. Information Sciences, 309, 102-118.
Zhao, X., Ma, Z., & Zhang, Z. (2017). A novel recommendation system in location-based social networks using distributed ELM. Memetic Computing, 10(3), 321-331.
Zheng, H., & Wu, J. (2017). Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization. Paper presented at the in Computer Communication and Networks (ICCCN), 2017 26th International Conference on IEEE, 1-9.
ر
منابع
جلالی، امید. (1393). سیستم پیشنهاددهنده هم‌پژوهشی (مورد مطالعه: شبکه اجتماعی کوثرنت). پایان نامه کارشناسی ارشد رشته مهندسی فناوری اطلاعات گرایش تجارت الکترونیک، دانشکده فنی و مهندسی، دانشگاه قم.
Amigó, E., Gonzalo, J., Artiles, J., & Verdejo, F. (2009). A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4), 461-486.
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations inlocation-based social networks: a survey.GeoInformatica, 19(3), 525-565.
Bradnova, V., Chernyavsky, M. M., Just, L., Haiduc, M., Kharlamov, S. P., Kovalenko, A. D., & Zarubin, P. L. (2003). Nuclear Clustering Quest in Relativistic Multifragmentation. Paper presented at the Few-Body Problems in Physics ’02, Vienna: Springer Vienna, Vol. 14, 241-244.
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