Where is the goldmine? Finding promising business locations through Facebook data analytics

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ

Evaluation of location’s attractiveness for business growth in smart development

The issue of a location’s attractiveness for business development in literature lacks approach, when attractiveness is assessed not as a set of factors which determine individual attractiveness, but as a locality’s ability to attract, maintain, and create business and investments. The contribution of the research to the discipline is a multi-criterion model of factors determining the location’s attractiveness for business development in the context of smart growth, as a methodological tool to evaluate and analyse the scientific problem in a question which is proposed by us. The attractiveness of a location for business development in the model is combined with the concept of smart development. A new and reliable instrument for decision-makers and managers is presented. An example of panel data analysis of 36 indicators and 3600 observations from 10 cross-sections of annual data for determining the role of quantitative indicators in attractiveness index is provided and timing lags influence is assessed. The method proposed is suitable for the attractiveness analysis of any location if the necessary data is availabl

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI