ABSTRACT
In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.
ABSTRACT
The Covid-19 pandemic has transformed our lives, and in order to aid in the prevention and spread of infection, a remote work style has rapidly proliferated. As this remote work style has proliferated, new problems have come to light. One problem is that managers cannot fully grasp the engagement level of subordinates such as in terms of absorption, dedication, and vigor due to limited in-person communications. However, as a substitute for in-person communications, online communications via text-based chat tools such as Slack and Microsoft's Teams have become popular. Recognizing the level of work engagement in a remote work setting is difficult, so we propose a new approach that estimates this level using text-based chat tools. To evaluate the proposal, we conduct experiments using actual Slack data. The experimental results reveal that the content of the conversations do not influence the level of work engagement, but the frequency of conversations among the teams and team members does. Therefore, we develop a machine learning model that estimates the level of work engagement using only the frequency and affiliation as features. The model estimates the work engagement level using true and predicted values at a correlation coefficient of 0.72. Since the proposed model uses only the frequency and affiliation, it is valuable in actual business situations. © 2021 Owner/Author.