مدل توصیه مکان‌های مورد علاقه با توجه به الگوی رفتاری افراد بر اساس لیست دوستان بر پایه یادگیری عمیق
الموضوعات :
1 - دانشگاه آزاد اسلامی واحد مشهد
2 - دانشگاه آزاد اسلامی واحد مشهد
الکلمات المفتاحية: خوشه‌بندی انتقال میانگین, شبکه عصبی کانولوشن, شبکه‌های اجتماعی, نقاط مورد علاقه (POI),
ملخص المقالة :
رشد سریع شبکه‌های اجتماعی مبتنی بر مکان، فرصتی عالی برای ارائه خدمات توصیه مکان‌های مورد علاقه به صورت هدفمند می‌باشد. یک وظیفه مهم برای توصیه دقیق نقاط جذاب و مورد علاقه کاربران در شبکه‌های اجتماعی مبتنی بر مکان، با توجه به چالش‌های متون غنی و پراکندگی داده‌ها، بررسی ویژگی‌های معنادار کاربران و نقاط مورد علاقه است. در این مقاله، یک روش جدید برای توصیه ترتیب دقیق بهترین نقاط مورد علاقه کاربران ارائه شده که ترکیبی از رویکردهای شبکه عصبی کانولوشن، خوشه‌بندی و دوستی می‌باشد. برای یافتن شباهت در رفتار دوستان صمیمی، از روش خوشه‌بندی انتقال میانگین استفاده می‌کنیم و فقط تأثیر الگوی رفتاری شبیه‌ترین دوست را به نسبت همه دوستان کاربر در نظر می‌گیریم. چارچوب جدید شبکه عصبی کانولوشن پیشنهادی با ۱۰ لایه می‌تواند طول و عرض جغرافیایی و شناسه مکان‌های مناسب بعدی را پیش‌بینی کرده و سپس بر اساس کوتاه‌ترین فاصله از الگوی رفتاری دوست مشابه، مکان‌های پیشنهادی را انتخاب کند. این رویکرد ترکیبی، در دو مجموعه داده شبکه‌های اجتماعی مبتنی بر مکان ارزیابی شده و نتایج تجربی نشان می‌دهد که استراتژی ما از روش‌های پیشرفته توصیه نقاط مورد علاقه دقیق‌تر عمل می‌کند.
المصادر:
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[39] J. Chen, Z. Liu, H. Wang, A. Nunez, and Z. Han, "Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network," IEEE Trans. Instrum. Meas., vol. 67, pp. 257-269, Dec. 2017.
[40] Yelp Dataset. Availabe online: (n.d.). https://www.yelp.com/dataset.
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[44] J. D. Zhang, C. Y. Chow, and Y. Li, "LORE: exploiting sequential influence for location recommendations," in Proc. 22nd. ACM Int. Symp. Adv. Geogr. Inf. Syst., Association for Computing Machinery, pp. 103-112, Dallas, TX, NY, USA, 3-7 Nov. 2014.
[45] H. Wang, M. Terrovitis, and N. Mamoulis, "Location recommendation in location-based social networks using user check-in data," in Proc. 21nd. ACM Int. Symp. Adv. Geogr. Inf. Syst., Association for Computing Machinery, pp. 364-373, Orlando, FL, USA, 5-8 Nov. 2013.
[1] T. Qian, B. Liu, Q. V. H. Nguyen, and H. Yin, "Spatiotemporal representation learning for translation-based POI recommendation," ACM Trans. Inf. Syst., vol. 37, no. 2, pp. 1-24, Jan. 2019.
[2] X. Li, D. Han, J. He, L. Liao, and M. Wang, "Next and next new POI recommendation via latent behavior pattern inference," ACM Trans. Inf. Syst., vol. 37, no. 4, pp. 1-28, Sept. 2019.
[3] W. Liu, H. Lai, J. Wang, G. Ke, W. Yang, and J. Yin, "Mix geographical information into local collaborative ranking for POI recommendation," World Wide Web, vol. 23, no. 1, pp. 131-152, Jan. 2020.
[4] Y. Wu, G. Zhao, K. Li, and X. Qian, "Long-and short-term preference learning for next POI recommendation," in Int. Conf. Inf. Knowl. Manag. Proc., Association for Computing Machinery, New York, NY, USA, pp. 2301-2304, Beijing, China, 3-7 Nov. 2019.
[5] R. Ding and Z. Chen, "RecNet: a deep neural network for personalized POI recommendation in location-based social networks," Int. J. Geogr. Inf. Sci., vol. 32, no. 8, pp. 1631-1648, Aug. 2018.
[6] T. N. Doan and E. P. Lim, "Modeling location-based social network data with area attraction and neighborhood competition," Data Min. Knowl. Discov., vol. 33, no. 1, pp. 58-95, Jan. 2019.
[7] D. Lian, K. Zheng, Y. Ge, L. Cao, E. Chen, and X. Xie, "GeoMF++: scalable location recommendation via joint geographical modeling and matrix factorization," ACM Trans. Inf. Syst., vol. 36, no. 3, pp. 1-29, Mar. 2018.
[8] M. Ye, P. Yin, W. C. Lee, and D. L. Lee, "Exploiting geographical influence for collaborative point-of-interest recommendation," in SIGIR'11-Proc. 34th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., Association for Computing Machinery, pp. 325-334, Beijing, China, 24-28 Jul. 2011.
[9] Z. Zhang, C. Li, Z. Wu, A. Sun, D. Ye, and X. Luo, "NEXT: a neural network framework for next POI recommendation," Front. Comput. Sci., vol. 14, no. 2, pp. 314-333, Apr. 2020.
Y. Gao, Z. Duan, W. Shi, J. Feng, and Y. Y. Chiang, "Personalized recommendation method of POI based on deep neural network," in Proc. Int. Conf. on Behavioral, Economic and Socio-Cultural Computing, 6 pp., Beijing, China, 28-30 Oct. 2019.
[10] M. A. Sit, C. Koylu, and I. Demir, "Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma," Int. J. Digit. Earth, vol. 12, no. 11, pp. 1205-1229, Jan. 2019.
[11] J. Zhang, Y. Xie, Q. Wu, and Y. Xia, "Medical image classification using synergic deep learning," Med. Image Anal., vol. 54, no. 1, pp. 10-19, May 2019.
[12] P. Y. Hao, W. H. Cheang, and J. H. Chiang, "Real-time event embedding for POI recommendation," Neurocomputing, vol. 349, pp. 1-11, 15 Jul. 2019.
[13] J. Yuan, X. Hou, Y. Xiao, D. Cao, W. Guan, and L. Nie, "Multi-criteria active deep learning for image classification," Knowledge-Based Syst., vol. 172, pp. 86-94, 15 May. 2019.
[14] R. Ranjbarzadeh and S. B. Saadi, "Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering," Meas. J. Int. Meas. Confed, vol. 150, pp. 230-269, Jan. 2020.
[15] H. Yin, W. Wang, H. Wang, L. Chen, and X. Zhou, "Spatial-aware hierarchical collaborative deep learning for POI recommendation," IEEE Trans. Knowl. Data Eng., vol. 29, no. 8, pp. 2537-2551, Aug. 2017.
[16] F. He and P. Wei, "Research on comprehensive point of interest (POI) recommendation based on spark," Cluster Comput., vol. 22, no. 4, pp. 9049-9057, Jul. 2019.
[17] L. Huang, Y. Ma, S. Wang, and Y. Liu, "An attention-based spatiotemporal LSTM network for next POI recommendation," IEEE Trans. Serv. Comput, vol. 12, no. 1, pp. 1-1, May 2019.
[18] L. Sun, "POI recommendation method based on multi-source information fusion using deep learning in location-based social networks," J. Inf. Process. Syst., vol. 17, no. 2, pp. 352-368, Apr. 2021.
[19] H. Wang, P. Li, Y. Liu, and J. Shao, "Towards real-time demand-aware sequential POI recommendation," Inf. Sci. (Ny), vol. 547, pp. 482-497, 8 Feb. 2021.
[20] J. Zeng, H. Tang, Y. Zhao, M. Gao, and J. Wen, "PR-RCUC: a POI recommendation model using region-based collaborative filtering and user-based mobile context," Mob. Networks Appl., vol. 26, no. 3, pp. 1-11, Jan. 2021.
[21] A. Ellahyani and M. El Ansari, "Mean shift and log-polar transform for road sign detection," Multimed. Tools Appl., vol. 76, no. 22, pp. 24495-24513, Nov. 2017.
[22] Q. Mahmood, A. Chodorowski, and M. Persson, "Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps," IRBM, vol. 36, no. 3, pp. 185-196, Jun. 2015.
[23] D. Comaniciu and P. Meer, "Mean shift: a robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, Aug. 2002.
Z. Liu, J. Liu, X. Xiao, H. Yuan, X. Li, J. Chang, and C. Zheng, "Segmentation of white blood cells through nucleus mark watershed operations and mean shift clustering," Sensors, vol. 15, no. 9, pp. 22561-22586, Sept. 2015.
[24] J. Michel, D. Youssefi, and M. Grizonnet, "Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 2, pp. 952-964, Jul. 2015.
[25] K. Fukunaga and L. D. Hostetler, "The estimation of the gradient of a density function, with applications in pattern recognition," IEEE Trans. Inf. Theory, vol. 21, no. 1, pp. 32-40, Jan. 1975.
[26] N. Karimi, R. Ranjbarzadeh Kondrood, and T. Alizadeh, "An intelligent system for quality measurement of Golden Bleached raisins using two comparative machine learning algorithms," Meas. J. Int. Meas. Confed, vol. 107, pp. 68-76, 1 Sept. 2017.
[27] P. J. Sudharshan, C. Petitjean, F. Spanhol, L. E. Oliveira, L. Heutte, and P. Honeine, "Multiple instance learning for histopathological breast cancer image classification," Expert Syst. Appl., vol. 117, pp. 103-111, 1 Mar. 2019.
[28] F. Ozyurt, T. Tuncer, E. Avci, M. Koc, and I. Serhatlioglu, "A novel liver image classification method using perceptual hash-based convolutional neural network," Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3173-3182, Apr. 2019.
[29] Y. Bengio, "Practical recommendations for gradient-based training of deep architectures," in Neural Networks: Tricks of the Trade, pp. 437-478, Springer, Berlin, Heidelberg, 2012.
[30] J. Dolz, C. Desrosiers, and I. Ben Ayed, "3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study," Neuroimage, vol. 170, pp. 456-470, 15 Apr. 2018.
[31] X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks," in Proc. of the 14th. Int. Conf. on Artificial Intelligence and Statistics, vol. 15, pp. 315-323, Fort Lauderdale, FL, USA, 11-13 Apr. 2011.
[32] W. Shang, K. Sohn, D. Almeida, and H. Lee, "Understanding and improving convolutional neural networks via concatenated rectified linear units," in Proc. 33rd. Int. Conf. on Machine Learning, vol 48, pp. 2217-2225, New York, NY, USA, 19-24 Jun. 2016.
[33] F. C. Morabito, M. Campolo, C. Ieracitano, and N. Mammone, "Deep learning approaches to electrophysiological multivariate time-series analysis," in Artificial Intelligence in the Age of Neural Networks and Brain Computing, ch. 11, pp. 219-243, Academic Press, 2018.
[34] W. Yin, H. Schutze, S. Schutze, B. Xiang, and B. Zhou, ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs, 2016. https://github.com/ (accessed January 4, 2021).
[35] F. Husain, B. Dellen, and C. Torras, "Scene understanding using deep learning," in Handbook of Neural Computation, ch. 20, pp. 373-382, Academic Press, 2017.
[36] M. Havaei, et al., "Brain tumor segmentation with deep neural networks," Med. Image Anal., vol. 35, pp. 18-31, Jan. 2017.
[37] N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," vol. 15, no. 56, pp. 1929-1958, 2014.
[38] T. Y. Kim and S. B. Cho, "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, vol. 182, pp. 72-81, Sept. 2019.
[39] J. Chen, Z. Liu, H. Wang, A. Nunez, and Z. Han, "Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network," IEEE Trans. Instrum. Meas., vol. 67, pp. 257-269, Dec. 2017.
[40] Yelp Dataset. Availabe online: (n.d.). https://www.yelp.com/dataset.
[41] E. Cho, S. A. Myers, and J. Leskovec, "Friendship and mobility: user movement in location-based social networks," in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1082-1090, San Diego, CA, USA. 21-24 Aug. 2011.
[42] J. Zhou, B. Liu, Y. Chen, and F. Lin, "UFC: a unified POI recommendation framework," Arab. J. Sci. Eng., vol. 44, no. 11, pp. 9321-9332, Nov. 2019.
[43] I. Rahman, et al., "Ergothioneine inhibits oxidative stress- and TNF-α-induced NF-κ B activation and interleukin-8 release in alveolar epithelial cells," Biochem. Biophys. Res. Commun., vol. 302, no. 4, pp. 860-864, Mar. 2003.
[44] J. D. Zhang, C. Y. Chow, and Y. Li, "LORE: exploiting sequential influence for location recommendations," in Proc. 22nd. ACM Int. Symp. Adv. Geogr. Inf. Syst., Association for Computing Machinery, pp. 103-112, Dallas, TX, NY, USA, 3-7 Nov. 2014.
[45] H. Wang, M. Terrovitis, and N. Mamoulis, "Location recommendation in location-based social networks using user check-in data," in Proc. 21nd. ACM Int. Symp. Adv. Geogr. Inf. Syst., Association for Computing Machinery, pp. 364-373, Orlando, FL, USA, 5-8 Nov. 2013.
