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PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations

PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations

Honey Jindal, Shalini Agarwal, Neetu Sardana
Copyright: © 2018 |Volume: 13 |Issue: 2 |Pages: 14
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781522542957|DOI: 10.4018/IJITWE.2018040106
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MLA

Jindal, Honey, et al. "PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations." IJITWE vol.13, no.2 2018: pp.56-69. http://doi.org/10.4018/IJITWE.2018040106

APA

Jindal, H., Agarwal, S., & Sardana, N. (2018). PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations. International Journal of Information Technology and Web Engineering (IJITWE), 13(2), 56-69. http://doi.org/10.4018/IJITWE.2018040106

Chicago

Jindal, Honey, Shalini Agarwal, and Neetu Sardana. "PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations," International Journal of Information Technology and Web Engineering (IJITWE) 13, no.2: 56-69. http://doi.org/10.4018/IJITWE.2018040106

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Abstract

This article describes how recommender systems are software applications or web portals that generate personalized preferences using information filtering techniques, with a goal to support decision-making of the users. Collaborative-based techniques are often used to predict the unknown preferences of the user based upon his past preferences or the preferences of the similar users that have already been identified. A user which has a high correlation with any group of users is known as white user whereas the users which have less correlation with any group of users are known as gray-sheep users. The presence of gray-sheep users affects the accuracy of the model, and generates inaccurate predictions. To improve the prediction accuracy, it is important to differentiate graysheep users from white users. Experimental results show that PowKMeans is effective in improving the prediction accuracy by 4.62%. It has also shown reduction in Mean Absolute Error by 0.7757.

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