  E-commerce sponsored search contributes an important part of revenue for the
e-commerce company. In consideration of effectiveness and efficiency, a
large-scale sponsored search system commonly adopts a multi-stage architecture.
We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad
retrieval and ad pre-ranking are collectively referred to as ad matching in
this paper. We propose an end-to-end neural matching framework (EENMF) to model
two tasks---vector-based ad retrieval and neural networks based ad pre-ranking.
Under the deep matching framework, vector-based ad retrieval harnesses user
recent behavior sequence to retrieve relevant ad candidates without the
constraint of keyword bidding. Simultaneously, the deep model is employed to
perform the global pre-ranking of ad candidates from multiple retrieval paths
effectively and efficiently. Besides, the proposed model tries to optimize the
pointwise cross-entropy loss which is consistent with the objective of predict
models in the ranking stage. We conduct extensive evaluation to validate the
performance of the proposed framework. In the real traffic of a large-scale
e-commerce sponsored search, the proposed approach significantly outperforms
the baseline.
