Analysis of Regression Tree Fitting Algorithms in Learning to Rank
This work addresses a gap in learning to rank for industry applications, but it is incremental as it builds on existing tree fitting methods.
The paper tackles the lack of analysis between least square error and weighted least square error principles in learning to rank, proposing a new least objective loss based error principle that shows moderate improvements in experiments on real-world datasets.
In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle. While in classification area, another tree fitting principle, weighted least square error, has been widely used, such as LogitBoost and its variants. However, there is a lack of analysis on the relationship between the two principles in the scenario of learning to rank. We propose a new principle named least objective loss based error that enables us to analyze the issue above as well as several important learning to rank models. We also implement two typical and strong systems and conduct our experiments in two real-world datasets. Experimental results show that our proposed method brings moderate improvements over least square error principle.