Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Better Tradeoff between Effectiveness and Efficiency in
  Pre-Ranking: A Learnable Feature Selection based Approach   In real-world search, recommendation, and advertising systems, the
multi-stage ranking architecture is commonly adopted. Such architecture usually
consists of matching, pre-ranking, ranking, and re-ranking stages. In the
pre-ranking stage, vector-product based models with representation-focused
architecture are commonly adopted to account for system efficiency. However, it
brings a significant loss to the effectiveness of the system. In this paper, a
novel pre-ranking approach is proposed which supports complicated models with
interaction-focused architecture. It achieves a better tradeoff between
effectiveness and efficiency by utilizing the proposed learnable Feature
Selection method based on feature Complexity and variational Dropout (FSCD).
Evaluations in a real-world e-commerce sponsored search system for a search
engine demonstrate that utilizing the proposed pre-ranking, the effectiveness
of the system is significantly improved. Moreover, compared to the systems with
conventional pre-ranking models, an identical amount of computational resource
is consumed.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph