Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset

Sneha Bohra; Amit Gaikwad; Ghanapriya Singh

doi:10.17485/IJST/v16i37.2065

Article

Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i37.2065

Year: 2023, Volume: 16, Issue: 37, Pages: 3121-3128

Original Article

Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset

Sneha Bohra^1*, Amit Gaikwad¹, Ghanapriya Singh²

¹Department of Computer Science and Engineering, School of Engineering & Technology, G H Raisoni University, Amravati, 444701, Maharashtra, India
²Department of Electronics & Communication Engg, National Institute of Technology Kurukshetra136119, Haryana, India

*Corresponding Author
Email: [email protected]

Received Date:14 August 2023, Accepted Date:02 September 2023, Published Date:03 October 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: The objective of this study is to design a machine learning based hybrid recommendation algorithm using Matrix Factorization and SVD to provide top – n movie recommendations. Methods: The proposed work is an integration of four well-known mechanisms namely Model-based Collaborative Matrix Factorization, KNN- based Clustering, SVD and Popularity based module to predict top-n recommendations. This work is implemented on movie-based datasets Movielens and tmdb-5000. The dataset is divided in 80: 20 for training and testing and we have used Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) performance metrics for evaluation of efficiency of proposed algorithm. Findings: The RMSE and MAE for the proposed hybrid model is 0.58 and 0.44 respectively. Novelty/Applications: The novelty of the work lies in two major aspects, firstly the linear ensemble of individual modules using popularity based, KNN based ,collabortiave MF and SVD and secondly the feedback evaluating mechanism which computes the relevancy of each recommendation generated. The proposed hybrid scheme focuses on user preferences and generates novel recommendations.

Keywords: kNearest Algorithm (KNN); Clustering; Matrix Factorization (MF); Singular Value Decomposition (SVD); Collaborative Filtering (CF)

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Copyright

© 2023 Bohra et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)