Paper Info
Title
Exploiting Geographical Data to Improve Recommender Systems for Business Opportunities in Urban Areas.
Abstract
The rapid urban expansion of the worldu0027s major cities has directly impacted peopleu0027s lives. In the urbanization process, it is common that business shops are open to attend the different needs and demands of the increasing number of citizens. This fact represents a business issue encouraging potential investments that could be harnessed to improve both urban economic environment and quality of urban life. However, many business opportunities are lost or not exploited properly due to the difficulty that investors, business owners, and marketers have to identify the right places where to open new stores. In this paper, we describe the implementation and evaluation of an approach to identify geographic areas with great potential to host business from a specific category. First, we adapt clustering algorithms to work with geographical data and, thus, partitioning a target city into business districts. Next, we use various recommendation algorithms to suggest the best categories for each business district. We conduct several experiments on Yelp data and our results show how geographical data and state-of-the-art algorithms can be used to mine business opportunities and predict adequate places to open new stores in urban areas.
Year
DOI
Venue
2019
10.1109/BRACIS.2019.00105
BRACIS
Field
DocType
Citations 
Recommender system,Urbanization,Urban life,Data visualization,Cluster analysis,Urban expansion,Environmental economics,Business
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Vinícius Ferreira100.34
Alan Valejo200.34
Paola Valdivia384.91
Jorge Carlos Valverde-Rebaza4798.11