Restaurant survival analysis with heterogeneous information - Lian, Jianxun; Zhang, Fuzheng; Xie, Xing; Sun, Guangzhong (International Conference on World Wide Web (WWW'17 Companion), 2017)
For shopkeepers, one of their biggest common concerns is whether their business will thrive or fail in the future. With the development of new ways to collect business data, it is possible to leverage multiple domains' knowledge to build an intelligent model for business assessment. In this paper, we discuss what the potential indicators are for the long-term survival of a physical store. To this end, we study factors from four pillars: geography, user mobility, user rating, and review text. We start by exploring the impact of geographic features, which describe the location environment of the retailer store. The location and nearby places play an important role in the popularity of the shop, and usually less competitiveness and more heterogeneity is better. Then we study user mobility. It can be viewed as supplementary to the geographical placement, showing how the location can attract users from anywhere. Another important factor is how the shop can serve and satisfy users. We find that restaurant survival prediction is a hard task that can not be solved simply using consumers' ratings or sentiment metrics. Compared with conclusive and well-formatted ratings, the various review words provide more insight of the shop and deserve in-depth mining. We adopt several language models to fully explore the textual message. Comprehensive experiments demonstrate that review text indeed have the strongest predictive power. We further compare different cities' models and find the conclusions are highly consistent. Although we focus on the class of restaurant in this paper, the method can be easily extended to other shop categories.