  Developing effective and efficient recommendation methods is very challenging
for modern e-commerce platforms. Generally speaking, two essential modules
named "Click-Through Rate Prediction" (\textit{CTR}) and "Conversion Rate
Prediction" (\textit{CVR}) are included, where \textit{CVR} module is a crucial
factor that affects the final purchasing volume directly. However, it is indeed
very challenging due to its sparseness nature. In this paper, we tackle this
problem by proposing multi-Level Deep Cascade Trees (\textit{ldcTree}), which
is a novel decision tree ensemble approach. It leverages deep cascade
structures by stacking Gradient Boosting Decision Trees (\textit{GBDT}) to
effectively learn feature representation. In addition, we propose to utilize
the cross-entropy in each tree of the preceding \textit{GBDT} as the input
feature representation for next level \textit{GBDT}, which has a clear
explanation, i.e., a traversal from root to leaf nodes in the next level
\textit{GBDT} corresponds to the combination of certain traversals in the
preceding \textit{GBDT}. The deep cascade structure and the combination rule
enable the proposed \textit{ldcTree} to have a stronger distributed feature
representation ability. Moreover, inspired by ensemble learning, we propose an
Ensemble \textit{ldcTree} (\textit{E-ldcTree}) to encourage the model's
diversity and enhance the representation ability further. Finally, we propose
an improved Feature learning method based on \textit{EldcTree}
(\textit{F-EldcTree}) for taking adequate use of weak and strong correlation
features identified by pre-trained \textit{GBDT} models. Experimental results
on off-line data set and online deployment demonstrate the effectiveness of the
proposed methods.
