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Product Recommendations System Survey

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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 and Bhushan Thakare. Article: Product Recommendations System Survey. International Journal of Computer Applications 131(9):36-38, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Sahil Pathan and Karan Panjwani and Nitin Yadav and Shreyas Lokhande and Bhushan Thakare},
	title = {Article: Product Recommendations System Survey},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {131},
	number = {9},
	pages = {36-38},
	month = {December},
	note = {Published by 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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Keywords

Cluster Model, Collaborative Filtering, Recommendation System, Search-based Methods.