  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.
