We propose and demonstrate the DisasterNeedFinder framework in order to provide appropriate information support for the Noto Peninsula Earthquake. In the event of a large-scale disaster, it is essential to accurately capture the ever-changing information needs.As a data-driven approach, we aim to pick up precise information needs at the site by integrally analyzing the location information of disaster victims and search information. The idea of assuming that the magnitude of information needs is not the volume of searches, but the degree of abnormalities in searches, enables an appropriate understanding of the information needs of the disaster victims in low population area. DNF has been continuously clarifying the information needs of disaster areas since the disaster strike, and has been recognized as a new approach to support disaster areas by being featured in the major Japanese media on several occasions. (For more information on this short paper, please refer to the Tsubouchi et al.[8].)

This study proposes a novel hybrid feedback approach called ”Boosting Feedback” to address the challenges of collecting correct data in machine learning research. Implicit feedback, derived from implicit behavioral logs, provides sufficient data quantity but may lack data quality due to various factors. Explicit feedback, obtained directly from users through surveys, offers high-quality data but is resource-intensive. The Boosting Feedback approach leverages implicit logs to augment the quantity of correct data from a single explicit feedback, doubling the available data by estimating opposite states from implicit logs. The method’s effectiveness is validated in actual recommendation experiment in the wild. Boosting Feedback offers a promising solution to improve data collection in machine learning research.

Recommendation systems help to predict user demand and improve the quality of services offered. While the performance of a recommendation system depends on the quality and quantity of feedback from users, the two major approaches to feedback sacrifice quality for quantity or vice versa; implicit feedback is more abundant but less reliable, while explicit feedback is more credible but harder to collect. Although a hybrid approach has the potential to combine the strengths of both kinds of feedback, the existing approaches using explicit feedback are not suitable for such a combination. In this study, we design a novel feedback suitable for the hybrid approach and use it improve the performance of a recommendation system. The system enables us to collect more varied and less biased feedback from users. It improves performance without requiring major changes to the inference model. It also provides a unique and rich source of information of the model itself. We demonstrate an application of Colorful Feedback showing how it can improve an existing recommendation model.