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Abstract

The popularity of Social networks, user demands, market realities, and technology developments are driving recommendation systems to explore new models of marketing and advertisements. Due to the great bulk of data on social media websites, the process of extracting hidden knowledge from data has become a hectic activity. For achieving this goal data mining techniques have been flourishing to discover interesting knowledge along with recommendation systems to suggest appropriate items to users based on this extracted knowledge. One of the most common obstacles in recommendation systems is a "cold-start" problem, which is related to users who do not indicate any behavior on social media. This paper aims to propose a solution for tackling this problem by using data mining techniques. In the next level, we enhance the recommendation method through Cuckoo algorithm to offer minimum number of items to get maximum feedback from users. Results indicate high performance of our proposed solution.

Item Type:	Article
Authors/Creators:	Forouzandeh, S and Aghdam, AR and Xu, S and Forouzandeh, S
Keywords:	data mining, recommender system, recommendation system, machine learning
Journal or Publication Title:	Computing in Science and Engineering
Publisher:	Ieee Computer Soc
ISSN:	1521-9615
DOI / ID Number:	10.1109/MCSE.2018.2875321
Copyright Information:	Copyright 2018 IEEE
Related URLs:	Google Scholar: Xu, S UTAS Profile: Xu, S
Item Statistics:	View statistics for this item

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