Location Based Business Recommendation Using Spatial Demand

Ashok Kumar P, Shiva Shankar G, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Abdulrahman Al-Ahmari and Mustufa Haider Abidi
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Ashok Kumar P: School of Computer Science and Engineering, VIT-Vellore, Tamil Nadu 632014, India
Shiva Shankar G: School of Computer Science and Engineering, VIT-Vellore, Tamil Nadu 632014, India
Praveen Kumar Reddy Maddikunta: School of Information Technology and Engineering, VIT-Vellore, Tamil Nadu 632014, India
Thippa Reddy Gadekallu: School of Information Technology and Engineering, VIT-Vellore, Tamil Nadu 632014, India
Abdulrahman Al-Ahmari: Raytheon Chair for Systems Engineering (RCSE), Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
Mustufa Haider Abidi: Raytheon Chair for Systems Engineering (RCSE), Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia

Sustainability, 2020, vol. 12, issue 10, 1-12

Abstract: Business locations is most important factor to consider before starting a business because the best location attracts more number of people. With the help of web search engines, the customers can search the nearest business location before visiting the business. For example, if a customer need to buy some jewel, he makes use of search engines to find the nearest jewellery shop. If some entrepreneur wants to start a new jewellery shop, he needs to find a best area where there is no jewellery shop nearby and there are more customers in need of jewel. In this paper, we propose an algorithm to find the best place to start a business where there is high demand and no (or very few supply). We measure the quality of recommendation in terms of average service time, customer-business ratio of our new algorithm by implementing in benchmark datasets and the results prove that our algorithm is more efficient than the existing kNN algorithm.

Keywords: business visualization; demand-supply analysis; business recommendation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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