| Issue |
E3S Web of Conf.
Volume 389, 2023
Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2023)
|
|
|---|---|---|
| Article Number | 07024 | |
| Number of page(s) | 14 | |
| Section | IT in Environmental Science | |
| DOI | https://doi.org/10.1051/e3sconf/202338907024 | |
| Published online | 31 May 2023 | |
Similarity based deep learning model for movie recommendation system
1 Department of Computer Science, Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai, India
2 School of Engineering, Dean, Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai, India
* Corresponding author: mahesh.smahesh@gmail.com
“Movie Recommendation Systems” helps user get relative & relevant items within millions of items. “Movie recommendation system’s” main task is to offer personalized content through information filtering. Here through this paper, we want to develop Similarity Based Deep Learning Model (SDLM) for automatic movie recommendation system. The projected technique is developed to identify the best rated movies and automatic movie recommendation system. This SDLM is a combination of “Spiking Neural Network (SNN)” and “Ebola Optimization Search Algorithm (EOSA)”. In the SNN, the EOSA is utilized to select optimal weighting parameters. The User Profile Correlation-Based Similarity (UPCS) is utilized along with proposed techniques to enable efficient movie recommendation system. To validate the proposed methodology, the movie databases is obtained from the online solutions. The proposed methodology is executed in MATLAB in addition performances can be assessed by “performance measures like recall, precision, accuracy, recall, specificity, sensitivity and F_Measure”. The projected methodology can be compared with the conventional methods such as “ODLM, Recurrent Neural Network (RNN) and Artificial Neural Network (ANN)” respectively.
Key words: recurrent neural network / user profile correlation-based similarity / similarity based deep learning model and spiking neural network
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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