Unbiased Learning to Rank with Biased Continuous Feedback
Yi Ren, Hongyan Tang, Siwen Zhu · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
It is a well-known challenge to learn an unbiased ranker with biased feedback. Unbiased learning-to-rank(LTR) algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, the existing unbiased LTR methods cannot properly handle continuous feedback, which are essential for many industrial applications, such as content recommender systems.