Publications
JOURNAL (INTERNATIONAL) Boosting Feedback: A Framework for Enhancing Ground Truth Data Collection
Kota Tsubouchi, Tatsuru Higurashi, Shuji Yamaguchi
2023 IEEE International Conference on Big Data (IEEE BigData 2023)
March 01, 2024
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.
Paper :
Boosting Feedback: A Framework for Enhancing Ground Truth Data Collection
(external link)