Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Journal Article
Source : International Journal of Computers and Applications
Url : https://www.tandfonline.com/doi/full/10.1080/1206212X.2022.2097769
Campus : Amaravati
School : School of Computing
Year : 2022
Abstract : In recent decades, recommendation systems are used in a variety of applications like social connections, movies, music, and venues. The existing algorithms has certain limitations like data sparsity, cold start problem, and poor scalability to degrade the movie recommendation performance. To address the aforementioned concerns, a new matrix factorization-based collaborative filtering algorithm is developed to enhance movie recommendation performance. In a neural collaborative filtering algorithm, an adaptive moment variance reduction gradient optimizer is applied in low-rank matrix factorization for representing the users and items in a low dimensional latent space to obtain effective movie recommendation performance. In this paper, the proposed algorithm performance is evaluated on three benchmark datasets; movielens 100K, 1M, and 10M datasets. Simulation results showed that the proposed algorithm obtained the difference of 0.08 and 0.056 roots mean square error value in movielens 1M and 10M datasets, which are better compared to the existing collaborative filtering-based deep learning algorithm.
Cite this Research Publication : V Lakshmi Chetana, Hari Seetha, " CF – AMVRGO: Collaborative Filtering based Adaptive Moment Variance Reduction Gradient Optimizer for Movie Recommendations", International Journal of Computers and Applications , Taylor & Francis group, July 2022.