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 e ective ranking results and at the same time obtain the results e ciently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to e ciently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively lter some items and rank the remaining items. However, most existing research of learning to e ciently 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 Ecommerce 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). E ectiveness of ecommerce 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 e ectiveness, e ciency and user experience in the real-world application. KEYWORDS cascade ranking; operational e-commerce search system; e ectiveness and e ciency; user experience * Both authors contributed equally to this study Permission to make digital
Liu et al. develop a cascade ranking model for a large-scale e-commerce search system and deploy it in Taobao.com REF .
Cascade Ranking for Operational E-commerce Search
KDD '17
Computer Science Mathematics