Cascade Ranking for Operational E-commerce Search
By Shichen Liu, Shicheng Liu, Fei Xiao, Wenwu Ou, Luo Si. 2017
118 citations recorded by the source corpus.
In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries. This paper presents novel research and thorough study of designing and deploying a Cascade model in a Large-scale Operational E-commerce Search application (CLOES), which deals with hundreds of millions of user queries per day with hundreds of servers. The challenge of the real-world application provides new insights for research: 1). Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2). Effectiveness of e-commerce search involves multiple types of user behaviors such as click and purchase, while most existing cascade ranking in search only models the click behavior. Based on these observations, a novel cascade ranking model is designed and deployed in an operational e-commerce search application. An extensive set of experiments demonstrate the advantage of the proposed work to address multiple factors of effectiveness, efficiency and user experience in the real-world application.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Information Systems
- Jimmy J. Lin
- Rodrigo Nogueira
- Wei Yang
- Kyunghyun Cho
- Ronak Pradeep
- Zeynep Akkalyoncu Yilmaz
- Peilin Yang
- Hui Fang
In Signal Processing
- Rodrigo Nogueira
- Peilin Yang
- Hui Fang
- Wei Yang
- Jimmy J. Lin
- Kyunghyun Cho
- Raphael Tang
- Yoshua Bengio
Citations in the corpus
- Multi-Stage Document Ranking with BERT (2019, methodology): “Known production deployments include the Bing web search engine (Pedersen, 2010) as well as Alibaba’s e-commerce search engine (Liu et al., 2017).”
- Anserini (2018, background): “What about reproducibility beyond simple bag-of-words ranking models? Although we have not explicitly addressed this (next) challenge, we believe that baseline bag-of-words ranking models also have an important role to play in modern multi-stage ranking architectures, which have been explored by res”
- Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020, methodology): “Known production deployments of this design include the Bing web search engine (Pedersen, 2010) as well as Alibaba’s ecommerce search engine (Liu et al., 2017).”