Geographic recommender systems in e-commerce based on population
Technological advancements have significantly enhanced e-commerce, helping customers find the best products. One key development is recommendation systems, which personalize the shopping experience and boost sales. This paper explores a novel geographic recommendation system that uses demographic da...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2525.pdf |
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| _version_ | 1832595616016891904 |
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| author | Mohamed Shili Osama Sohaib |
| author_facet | Mohamed Shili Osama Sohaib |
| author_sort | Mohamed Shili |
| collection | DOAJ |
| description | Technological advancements have significantly enhanced e-commerce, helping customers find the best products. One key development is recommendation systems, which personalize the shopping experience and boost sales. This paper explores a novel geographic recommendation system that uses demographic data, such as population density, age, and income, to refine recommendations. By integrating geographic and demographic information, like the population size of a country, businesses can tailor their offerings to regional preferences. This targeted approach aims to make recommendations more relevant by considering the behaviors and needs of different geographic areas. We sourced population data from The National Institute of Statistics (Tunisia, INS). This approach improves the importance of product recommendations for particular locations by customizing them based on demographic and geographic measures. The technique creates a better context-aware recommendation system that boosts customer happiness and business proceeds by fusing consumer behavior with extensive demographic data. The method also includes a mathematical model that considers population intensity to refine further recommendations established on the regional model. |
| format | Article |
| id | doaj-art-628980e78f7b4c139593135deeb45743 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-628980e78f7b4c139593135deeb457432025-01-18T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e252510.7717/peerj-cs.2525Geographic recommender systems in e-commerce based on populationMohamed Shili0Osama Sohaib1Innov’COM Laboratory, National Engineering School of Carthage, University of Carthage, Carthage, TunisiaSchool of Business, American University of Ras al Khaimah, Ras al Khaimah, United Arab EmiratesTechnological advancements have significantly enhanced e-commerce, helping customers find the best products. One key development is recommendation systems, which personalize the shopping experience and boost sales. This paper explores a novel geographic recommendation system that uses demographic data, such as population density, age, and income, to refine recommendations. By integrating geographic and demographic information, like the population size of a country, businesses can tailor their offerings to regional preferences. This targeted approach aims to make recommendations more relevant by considering the behaviors and needs of different geographic areas. We sourced population data from The National Institute of Statistics (Tunisia, INS). This approach improves the importance of product recommendations for particular locations by customizing them based on demographic and geographic measures. The technique creates a better context-aware recommendation system that boosts customer happiness and business proceeds by fusing consumer behavior with extensive demographic data. The method also includes a mathematical model that considers population intensity to refine further recommendations established on the regional model.https://peerj.com/articles/cs-2525.pdfE-commerceRecommendation systemGISPopulation-data |
| spellingShingle | Mohamed Shili Osama Sohaib Geographic recommender systems in e-commerce based on population PeerJ Computer Science E-commerce Recommendation system GIS Population-data |
| title | Geographic recommender systems in e-commerce based on population |
| title_full | Geographic recommender systems in e-commerce based on population |
| title_fullStr | Geographic recommender systems in e-commerce based on population |
| title_full_unstemmed | Geographic recommender systems in e-commerce based on population |
| title_short | Geographic recommender systems in e-commerce based on population |
| title_sort | geographic recommender systems in e commerce based on population |
| topic | E-commerce Recommendation system GIS Population-data |
| url | https://peerj.com/articles/cs-2525.pdf |
| work_keys_str_mv | AT mohamedshili geographicrecommendersystemsinecommercebasedonpopulation AT osamasohaib geographicrecommendersystemsinecommercebasedonpopulation |