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JACIII Vol.29 No.3 pp. 574-582
doi: 10.20965/jaciii.2025.p0574
(2025)

Research Paper:

Predicting the Number of Clicks in a Local Information Sharing System Focusing on Generational Information

Daichi Inoue* ORCID Icon 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

Received:
January 23, 2025
Accepted:
February 17, 2025
Published:
May 20, 2025
Keywords:
social media, flyer image, microevent, CNN, generation information
Abstract

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

Proposed model and research process

Cite this article as:
D. Inoue and S. Matsumoto, “Predicting the Number of Clicks in a Local Information Sharing System Focusing on Generational Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.3, pp. 574-582, 2025.
Data files:
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Last updated on May. 19, 2025