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Reseach Article

Product Recommendations System Survey

by Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 9
Year of Publication: 2015
Authors: Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare
10.5120/ijca2015907394

Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare . Product Recommendations System Survey. International Journal of Computer Applications. 131, 9 ( December 2015), 36-38. DOI=10.5120/ijca2015907394

@article{ 10.5120/ijca2015907394,
author = { Sahil Pathan, Karan Panjwani, Nitin Yadav, Shreyas Lokhande, Bhushan Thakare },
title = { Product Recommendations System Survey },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 9 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number9/23481-2015907394/ },
doi = { 10.5120/ijca2015907394 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:28.492095+05:30
%A Sahil Pathan
%A Karan Panjwani
%A Nitin Yadav
%A Shreyas Lokhande
%A Bhushan Thakare
%T Product Recommendations System Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 9
%P 36-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommendation Systems are used to increase the growth of various online businesses. E-commerce players are utilizing such systems to get high sales. Such systems make use of statistics and data from user behaviour e.g. Purchase history, product ratings. So, decision to display a specific product from a specific category is taken after considering such parameters. In Hyper-Local based services (Locality Based) recommendation systems operate in a challenging environment. Such as, new customers have too much limited information associated, less purchase history, no product ratings etc. Secondly a large retailer have too much categories to choose from. Last, users tends have scattered data-less patterns. In order to handle such information mainly three methods are available: search-based methods, collaborative filtering and cluster models. These methods are more suitable in a vast user base environment. Whereas, in small scale environments a set of customers whose purchased and rated products overlaps with a current user's purchased and rated products are subject to a simple measurements.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Cluster Model Collaborative Filtering Recommendation System Search-based Methods.