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A prediction model for benefitting e-commerce through usage of regional data: A new framework

IAES International Journal of Artificial Intelligence · Vol. 10 No. 4 (2021) · hlm. 1009–1018

DOI
10.11591/ijai.v10.i4.pp1009-1018
ISSN
2252-8938, 2089-4872
Terbit
2021-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Today during ‘Covid-20’, people are more inclined towards online shopping. In general practice, analysis of browsing history and customer’s micro behaviour against online shopping habits have been used for future suggestions. Due to this, the predictions made were suffereing from over-similarity problem and the user was unable to find any novelty in the recommended items. Observing these issues, e-shopping quality can be enhanced by adding a factor other than similarity. The current research suggests and advertise those products which belongs to a person’s region. For this research work the data has been collected on the basis of area-wise, like, country-based seggregation. Here the considered dataset belongs to country, ‘India’, its culture, its handicraft and its citizens. Datasets and their combinations based on multiple attributes are input for the proposed predictive system. In this paper, existing data is also considered for collecting customers demographic details which is further mapped with the area-wise dataset. Also, a framework has been proposed which uses database and user query as input for its predictive system in order to generate default suggestions for the user other than the submitted query also.

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  • Manav Rachna International Institute of Research and Studies
  • Amity University
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Rekan penulisOrganisasiArtikel bersama
Yash VermaAmity University1
Madhulika BhatiaManav Rachna International Institute of Research and Studies1
Shaveta BhatiaManav Rachna International Institute of Research and Studies1
Mridula BatraManav Rachna International Institute of Research and Studies1
Priyanka RaiAfiliasi tidak diketahui1
Anshu SharmaManav Rachna International Institute of Research and Studies1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A prediction model for benefitting e-commerce through usage of regional data: A new framework</dc:title>
	<dc:creator>Singhal, Shefali</dc:creator>
	<dc:creator>Tanwar, Poonam</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Intelligence; Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Classification and clustering algorithms, Data extraction, E-commerce, ETL process, Predictive modelling</dc:subject>
	<dc:description xml:lang="en-US">Today during ‘Covid-20’, people are more inclined towards online shopping. In general practice, analysis of browsing history and customer’s micro behaviour against online shopping habits have been used for future suggestions. Due to this, the predictions made were suffereing from over-similarity problem and the user was unable to find any novelty in the recommended items. Observing these issues, e-shopping quality can be enhanced by adding a factor other than similarity. The current research suggests and advertise those products which belongs to a person’s region. For this research work the data has been collected on the basis of area-wise, like, country-based seggregation. Here the considered dataset belongs to country, ‘India’, its culture, its handicraft and its citizens. Datasets and their combinations based on multiple attributes are input for the proposed predictive system. In this paper, existing data is also considered for collecting customers demographic details which is further mapped with the area-wise dataset. Also, a framework has been proposed which uses database and user query as input for its predictive system in order to generate default suggestions for the user other than the submitted query also.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Nil</dc:contributor>
	<dc:date>2021-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20902</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i4.pp1009-1018</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 4: December 2021; 1009-1018</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20902/13251</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>