Mining Business Opportunities from Location-based Social Networks

Urbanization's rapid progress has modernized a large number of human beings' lives. This urbanization progress is accompanied by the increase of a variety of shops (e.g., restaurants and fitness centers) to meet the increasing citizens, which means business opportunities for the investors. Nevertheless, it is difficult for the investors to catch such opportunities because opening what kind of business at which place is not easy to decide. In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district. Specifically, we exploit the data from location-based social networks (LBSNs) to mine the business opportunities, guiding the business owners to open new commercial shops in certain categories at a particular area. First, we define the properties of a business district and propose a greedy algorithm to partition a city into different districts. Next, we propose an embedding model to learn latent representations of categories, which captures the functional correlations among business categories. Furthermore, we propose a ranking model based on the pairwise loss to recommend categories for a specific district. Finally, we conduct experiments on Yelp data, and experimental results show that our proposed method outperforms the baseline methods and resolves the problem well.