Business performance prediction in location-based social commerce

被引：18

作者：

Chang, Xiaohui ^{[1
]}

Li, Jiexun ^{[2
]}

机构：

[1] Oregon State Univ, Coll Business, Corvallis, OR 97331 USA

[2] Western Washington Univ, Coll Business & Econ, Dept Decis Sci, Bellingham, WA 98225 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2019年 / 126卷

关键词：

Social commerce; Location-based services; Site selection; Business performance prediction; COMPETITION; SEARCH;

D O I：

10.1016/j.eswa.2019.01.086

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Social commerce and location-based services provide a data platform for coexisting and competing businesses in geographical neighborhoods. Our research is aimed at mining data from such platforms to gain valuable insights for better support to strategic and operational business decisions. We develop a computational framework for predicting business performance that takes into account both intrinsic (e.g., attributes) and extrinsic (e.g., competitions) factors. Our experiments on synthetic and real datasets demonstrated superiority of a hybrid prediction model that adopts both link-based and context-based assumptions. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：112 / 123

页数：12

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