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An Influential Recommendation System Usage for General Users

Nikhat Akhtar, Devendera Agarwal. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Nikhat Akhtar, Devendera Agarwal

Nikhat Akhtar and Devendera Agarwal. An Influential Recommendation System Usage for General Users. Communications on Applied Electronics 5(7):5-9, July 2016. BibTeX

	author = {Nikhat Akhtar and Devendera Agarwal},
	title = {An Influential Recommendation System Usage for General Users},
	journal = {Communications on Applied Electronics},
	issue_date = {July 2016},
	volume = {5},
	number = {7},
	month = {Jul},
	year = {2016},
	issn = {2394-4714},
	pages = {5-9},
	numpages = {5},
	url = {},
	doi = {10.5120/cae2016652315},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Recommender systems are extensively seen as an effective means to combat information overload, as they redound us both narrow down the number of items to choose. They are seen as assistance us make better decisions at a lower transaction cost. Hence, recommender systems have become omnipresent in e-commerce and are also increasingly used in services in different other domains both online and offline where the number of items exceeds our potentiality to consider them all individually. The research papers recommender systems are software applications or systems that help individual users to discover the most relevant research papers to their needs. These systems use filtering techniques to create recommendations. These techniques are categorized majorly into collaborative-based filtering, content-based technique, and hybrid algorithm. In addition, they assist in decision making by providing product information both personalized and non-personalized, summarizing community opinion, search research papers, and providing community critiques. As a result, recommender systems have been shown to ameliorate the decision.


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Recommendations System, Tagging, Information Retrieval, E-Commerce, Collaborative Filtering.