Research Paper:
Predicting the Number of Clicks in a Local Information Sharing System Focusing on Generational Information
Daichi Inoue*
and Shimpei Matsumoto**
*D2C Inc.
Tokyo Shiodome Building, 1-9-1 Higashi-Shinbashi, Minato-ku, Tokyo 105-7314, Japan
**Hiroshima Institute of Technology
2-1-1 Miyake, Saeki-ku, Hiroshima 731-5193, Japan
In recent years, “Tame-map” has emerged as a social media platform for local revitalization. It is a web application for sharing local information. It enables users to conveniently post and view information on local events in their daily lives. Because many “Tame-map” users are likely to participate in events, increasing the number of views is an important issue from the perspective of regional revitalization. The design relies on the organizer’s experience and intuition, and there is no established method for developing a design that attracts a large number of viewers. Therefore, if the number of visitors can be predicted in advance, it is feasible to reconsider the design of flyers based on this information. In addition, in click-through rate (CTR) prediction, which is an aspect of advertising analysis, it has been revealed that predicting the user attributes of viewers contributes to improving the prediction accuracy. However, in the “Tame-map” system, user attributes of viewers do not exist. In this study, we aim to clarify the extent to which considering the generational tags assigned to events would impact the prediction of click counts.

Proposed model and research process
- [1] Z. Li, X. Fang, and O. Sheng, “A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions,” ACM Trans. Manage. Inf. Syst. Vol.9, No.1, pp. 1-26, 2018. https://doi.org/10.1145/3131782
- [2] I. Paik and H. Mizugai, “Recommendation system using weighted TF-IDF and naive bayes classifiers on RSS contents,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.6, pp. 631-637, 2010. https://doi.org/10.20965/jaciii.2010.p0631
- [3] U. Shardanand and P. Maes, “Social information filtering: Algorithms for automating “word of mouth”,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 210-217, 1995. https://doi.org/10.1145/223904.223931
- [4] S. Matsumoto, T. Tateyama, K. Okimoto, and Y. Shimizu, “Tamemap, Everybody’s Town BBS:—Development and Operation of a Smartphone Application to Share the Information of Micro Community Activities—,” IEEJ Trans. on Electronics, Information and Systems, Vol.140, No.8, pp. 925-938, 2020 (in Japanese). https://doi.org/10.1541/ieejeiss.140.925
- [5] Y. Iwazaki, “Deep CTR Prediction in Facebook Ads,” Proc. of the Annual Conf. of Japanese Society for Artificial Intelligence, Article No.4Pin114-4Pin114, 2018 (in Japanese). https://doi.org/10.11517/pjsai.JSAI2018.0_4Pin114
- [6] T. Demizu, Y. Fukazawa, and H. Morita, “Predicting CTR of In-feed Ads Considering Time Decay Using Deep Learning,” Trans. of Information Processing Society of Japan, Vol.62, No.8, pp. 292-301, 2021 (in Japanese).
- [7] CNET Japan, “Facebook Japan CEO Hasegawa talks about the true reason for stepping down-Exclusive Long Interview” (in Japanese). https://japan.cnet.com/article/35139021/ [Accessed February 10, 2022]
- [8] M. Richardson, E. Dominowska, and R. Ragno, “Predicting clicks: estimating the click-through rate for new ads,” Proc. of the 16th Int. Conf. on World Wide Web, pp. 521-530, 2007. https://doi.org/10.1145/1242572.1242643
- [9] W. Zhang, J. Qin, W. Guo, R. Tang, and X. He, “Deep learning for click through rate estimation. Proc. of the Thirtieth Int. Joint Conf. on Artificial Intelligence, pp. 4695-4703, 2021.
- [10] H. Cheng, R.van Zwol, J. Azimi, E. Manavoglu, R. Zhang, Y. Zhou, and V. Navalpakkam, “Multimedia features for click prediction of new ads in display advertising,” Proc. of the 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD ’12), Association for Computing Machinery, New York, USA, pp. 777-785, 2012.
- [11] H. Seshime et al., “Click-Through Rate Prediction of Display Advertisement with Deep Multi-Patch Method,” Proc. of the Annual Conf. of the Japanese Society for Artificial Intelligence, 2020 (in Japanese). https://doi.org/10.11517/pjsai.JSAI2020.0_1H4OS12b03
- [12] Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin, “Field-Aware Factorization Machines for CTR Prediction,” Proc. of the 10th ACM Conf. on Recommender Systems, pp. 43-50, 2016. https://doi.org/10.1145/2959100.2959134
- [13] D. Inoue and S. Matsumoto, “Predicting the number of clicks on event flyer images by focusing on the title information,” 2023 IEEJ Annual Conf. on Electronics Information and Systems, GS4-5, pp. 1285-1290, 2023.
- [14] D. Inoue and S. Matsumoto, “Predicting CTR of Regional Flyer Images Using CNN,” 2022 12th Int. Congress on Advanced Applied Informatics (IIAI-AAI), pp. 525-528, 2022. http://dx.doi.org/10.1109/IIAIAAI55812.2022.00107
- [15] D. Inoue and S. Matsumoto, “Verification of the presence or absence of the event location in predicting CTR for event flyer images,” Proc. IEEE Electronic, Information and Systems Division Conf., GS2-3, pp. 1012-1017, 2022.
- [16] D. Inoue and S. Matsumoto, “Generation Prediction Focusing on Title Information in a Local Micro-Event Information Sharing,” The papers of Technical Meeting on Information Systems, IEE Japan, pp. 57-60, 2023.
- [17] C. Tianqi and C. Guestrin, “Xgboost: A scalable tree boosting system,” Proc. of the 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2016. https://doi.org/10.1145/2939672.2939785
- [18] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3146-3154, 2017.
- [19] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90
- [20] A. Muhammad, M. T. B. Iqbal, and S. H. Bae, “Revisiting internal covariate shift for batch normalization,” IEEE Trans. on Neural Networks and Learning Systems, Vol.32, No.11, pp. 5082-5092, 2020. https://doi.org/10.1109/tnnls.2020.3026784
- [21] M. Kuhn and K. Johnson, “Applied Predictive Modeling,” Springer New York, 2013.
- [22] I2tutorials, “Differences Between MSE and RMSE.” https://www.i2tutorials.com/differences-between-mse-and-rmse/ [Accessed March 3, 2023]
- [23] D. Kadowaki, T. Sakata, K. Hosaka, and Y. Hiramastu, “Kaggle’s Winning Data Analysis Technology,” Gijutsu-Hyohron Co., Ltd., 2019 (in Japanese).
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.