Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization

doi:10.23940/ijpe.22.04.p8.298306

Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (4): 298-306.doi: 10.23940/ijpe.22.04.p8.298306

Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization

D. R. Kumar Raja^a, G. Hemanth Kumar^b, Syed Muzamil Basha^c, and Syed Thouheed Ahmed^a,*

^aSchool of Computing and Information Technology, REVA University, Bengaluru, 560064, India;
^bSchool of Electronics and Communication Engineering, REVA University, Bengaluru, 560064, India;
^cSchool of Computer Science and Engineering, REVA University, Bengaluru, India

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: syedthouhed.ahmed@reva.edu.in
About author:D R Kumar Raja is an Associate Professor, School of Computing and Information Technology, REVA University, Bengaluru, India.. His research interests include Internet of Things, Robotics and Automation.
G Hemanth Kumar is an Assistant professor, School of Electronics and Communication Engineering, Reva University, Bengaluru, India. His research interests include Internet of Things, Robotics and Automation.
Syed Muzamil Basha is an associate professor, School of Computer Science and Engineering. His research interests include Artificial Intelligence, Machine Learning and Automation.
Syed Thouheed Ahmed is an Assistant Professor, School of Computing and Information Technology, REVA University, Bengaluru, India.. His research interests include Internet of Things, Robotics and Automation.

Abstract

Abstract: The prompt progression of web data and the number of web visitors creates a latent information overload problem and complicates data mining to select the right items on the web. E-commerce websites and applications manage information overload using several information filtering techniques such as personalized recommendation systems. The recommendation system creates a list of products to helps users. The proposed NOMINATE methodology offers characteristic values ??for definite elements from the LOD source as input for matrix factorization. This helps NOMINATE to retrieve user-specific functions. Subsequently studying the functions, the proposed approach produces an enhanced cluster of consumers by the weight of every user above the desired values ??of the element characteristics. For this, the NOMINATE methodology uses the particle swarm optimization (PSO) algorithm and k-means clustering algorithm. Using clustered results, the NOMINATE approach estimaties the centrality of proximity among other users in the cluster and identifies an experienced user for each cluster. In addition, the proposed recommendation scheme produces a set of characteristic values for each user based on the adept information of the users in the respective clusters.

Key words: K‐means, LOD, Top‐N recommendations, PMF, recommender system.

D. R. Kumar Raja, G. Hemanth Kumar, Syed Muzamil Basha, and Syed Thouheed Ahmed. Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization [J]. Int J Performability Eng, 2022, 18(4): 298-306.

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Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization

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