ROSE: retail outlet site evaluation by learning with both sample and feature preference.
DOI: 10.1145/1645953.1646129 Conference: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009
It is critical for retail enterprises to select good sites or locations to open their stores, especially in current competitive retail market. However, evaluating the goodness of sites in real business applications is a complex problem. That is, how to judge whether the market around a store site is good? We don't know the exact mechanism of how a site can be good and it is hard to have correct site goodness values as supervised labels. The Retail Outlet Site Evaluation (ROSE) tool is designed to learn the site evaluation model by integrating city geographic & demographic data and two kinds of expert knowledge: sample preference and feature preference. The feature preference information can help greatly reduce the required number of sample preferences. It enables our application practicable because it is almost impossible to give such amount of sample preference pairs manually by experts when ranking hundreds of data points. In the experiment and case study part, we show that the ROSE tool can achieve good results and useful for users to do site evaluation work in real cases.
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ABSTRACT: Industrial and Commercial Bank of China (ICBC), the world's largest publicly traded bank as measured by market capitalization, deposit volume, and profitability, has a network of over 16,000 branches. This network is an important core competency and is fundamental to ICBC business development. To keep its leading position in the fast-changing and competitive China market, ICBC needed to reconfigure its branch locations and service capabilities to match the regional economy and customer distribution; therefore, it had to quickly identify new high-potential market areas in which to open branches. ICBC partnered with IBM to customize an operations research-based branch network optimization system, Branch Reconfiguration (BR), which it has implemented in over 40 major cities in China. In a typical major city (e.g., Suzhou), ICBC attributes US $1.04 billion in increased deposits to BR. The BR project is an example of successfully using operations research and management sciences to transform the service channels of a large bank in a manner that will continue to improve the bank's business development and decision making.Interfaces 02/2012; 42(1):33-44. DOI:10.1287/inte.1110.0614 · 0.42 Impact Factor
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