Paper Info
Title
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
Abstract
ABSTRACTIn 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.
Year
DOI
Venue
2021
10.1145/3404835.3462979
Research and Development in Information Retrieval
Keywords
DocType
Citations 
pre-ranking, effectiveness, efficiency, feature selection
Conference
1
PageRank 
References 
Authors
0.38
0
9
Name
Order
Citations
PageRank
Xu Ma110.38
Pengjie Wang2141.27
Hui Zhao310.72
Shaoguo Liu432.46
Chuhan Zhao510.38
Wei Lin622924.46
Kuang-Chih Lee7356.44
Jian Xu830120.18
Bo Zheng91210.73