一种新的数据驱动的网站自动推荐与选择方法
A Novel Approach for Data-Driven Automatic Site Recommendation and Selection
摘要：
本文提出了一种新的，通用的，自动化的数据驱动的选址方法。选址是任何公司做出的最关键和最重要的决定之一。这一决定取决于场地的各种因素，包括社会经济、地理、生态以及公司的具体要求。现有的选址方法（经济学家常用）是手动的，主观的，不可扩展的，特别是对于大数据。所提出的选址方法是鲁棒的，高效的，可扩展的，并且能够处理大数据中出现的挑战。为了评估所提出的方法的有效性，它是评估的真实数据（从德国联邦统计局收集）的约200个影响因素，被认为是由经济学家在德国的超市选址（Lidl，EDEKA，和NP）。评价结果表明，现有超市选址与推荐选址有较大的重叠（86.4%）。此外，该方法还推荐了许多应该开设商店的超级市场的地点（328）。
英文摘要：
This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socio-economic, geographical, ecological, as well as specific requirements of companies. The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data. The presented method for site selection is robust, efficient, scalable, and is capable of handling challenges emerging in Big Data. To assess the effectiveness of the presented method, it is evaluated on real data (collected from Federal Statistical Office of Germany) of around 200 influencing factors which are considered by economists for site selection of Supermarkets in Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big overlap (86.4 \%) between the sites of existing supermarkets and the sites recommended by the presented method. In addition, the method also recommends many sites (328) for supermarket where a store should be opened.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
