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Opinion Observer: Recommendation System on E-Commerce Website

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 105 - Number 14
Year of Publication: 2014
Authors:
Mohammad Daoud
S. K Naqvi
Asad Ahmad
10.5120/18449-9842

Mohammad Daoud, S k Naqvi and Asad Ahmad. Article: Opinion Observer: Recommendation System on E-Commerce Website. International Journal of Computer Applications 105(14):37-42, November 2014. Full text available. BibTeX

@article{key:article,
	author = {Mohammad Daoud and S.k Naqvi and Asad Ahmad},
	title = {Article: Opinion Observer: Recommendation System on E-Commerce Website},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {105},
	number = {14},
	pages = {37-42},
	month = {November},
	note = {Full text available}
}

Abstract

To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Recommender systems are being utilized by an ever-increasing number of E-commerce sites to help consumers discover products to buy. The most of existing system gives the recommendation based on best selling product, on demographics of the consumer, or on an analysis of the past buying behavior of the consumer. Our purposed system based on the consumer reviews and advanced multi-criteria search engine. In this paper, we used a text mining approach to mine product features, opinions and their semantic similarity from Web opinion sources. The consumer can clearly see the strengths and Weaknesses of each product in the minds of existing consumer's opinion. The system assists on-line shoppers or goal oriented shopper by suggesting the most effective navigation products for their specified criteria and preferences.

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