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PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix
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Author(s): Bathrinath S. (Kalasalingam University, India), Saranyadevi S. (Kalasalingam University, India), Thirumalai Kumaran S. (Kalasalingam University, India)and Saravanasankar S. (Kalasalingam University, India)
Copyright: 2019
Pages: 14
Source title:
Handbook of Research on Green Engineering Techniques for Modern Manufacturing
Source Author(s)/Editor(s): M. Uthayakumar (Kalasalingam University, India), S. Aravind Raj (Vellore Institute of Technology, India), Tae Jo Ko (Yeungnam University, South Korea), S. Thirumalai Kumaran (Kalasalingam University, India)and J. Paulo Davim (University of Aveiro, Portugal)
DOI: 10.4018/978-1-5225-5445-5.ch006
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Applications of web data mining is the prediction of user behavior with respect to items. Recommender systems are being applied in knowledge discovery techniques to the problem of making decisions on personalized recommendation of information. Traditional CF approaches involve the amount of effort increases with number of users. Hence, new recommender systems need to be developed to process high quality recommendations for large-scale networks. In this chapter, a model for UAR matrix construction method for item rank calculations, a Page Rank-based item ranking approach are proposed. The analysis of various techniques for computing item-item similarities to identify relationship between the selected items and to produce a qualified recommendation for users to acquire the items as their wish. As a result, the new item rank-based approaches improve the quality of recommendation outcome. Results show that the proposed UAR method outperforms than the existing method. The same method is applied for the large real-time rating dataset like Movie Lens.
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