Publications
カンファレンス (国際) ColorfulFeedback: Enhancing Interest Prediction Performance through Multi-dimensional Labeled Feedback from Users
Yuki Maeda (Keio University), Shuji Yamaguchi, Tatsuru Higurashi, Kota Tsubouchi
14th ACM International Web Search and Data Mining conference (WSDM2021)
2021.3.10
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 ColorfulFeedback showing how it can improve an existing recommendation model.
Paper :
ColorfulFeedback: Enhancing Interest Prediction Performance through Multi-dimensional Labeled Feedback from Users
（外部サイト）