Title: Collaborative filtering model of book recommendation system

Authors: Xiaoqiang Guo; Lichao Feng; Yalou Liu; Xiuli Han

Addresses: College of Science, North China University of Science and Technology, Tangshan, Hebei 063009, China ' College of Science, North China University of Science and Technology, Tangshan, Hebei 063009, China ' College of Mining Engineering, North China University of Science and Technology, TangShan 063009, China ' College of Mining Engineering, North China University of Science and Technology, TangShan 063009, China

Abstract: With the rapid development of information technology and internet, people from an era of scarcity gradually entered the era of information overload. For information-consumers, finding themselves interested in information from a large amount of information is a very difficult task. As regard to information producers, letting the production information stand out and getting the attention of the masses of users is also a very difficult task. To solve this contradiction, first, we establish a decorrelation principal component analysis model based on the correlation theory to obtain the main factors affecting the user evaluation of books. Secondly, we establish a predictive scoring system based on linear regression theory which can predict book ratings. Finally, we establish a collaborative filtering model of book recommendation.

Keywords: principal component analysis; PCA; regression prediction; collaborative filtering; book recommendation systems; book recommendations; recommender systems; user evaluation.

DOI: 10.1504/IJAMC.2016.080974

International Journal of Advanced Media and Communication, 2016 Vol.6 No.2/3/4, pp.283 - 294

Received: 26 Feb 2016
Accepted: 18 May 2016

Published online: 10 Dec 2016 *

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