NSGA-II optimized deep autoencoders for enhanced multi-criteria recommendation system

Recommendation systems are decision-support systems used by e-commerce enterprises to evaluate customers’ preferences and suggest items based on their interests. Moreover, they also tackle the problem of information overload. Multi-criteria recommendation systems differ from standard approaches by u...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 123; p. 110159
Main Authors: Rajput, Ishwari Singh, Tewari, Anand Shanker, Tiwari, Arvind Kumar
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2025
Subjects:
Autoencoder
Collaborative filtering
Deep learning
Meta-heuristics
Multi-criteria
NSGA-II
Recommendation system
Deep learning
NSGA-II
Collaborative filtering
Autoencoder
Multi-criteria
Recommendation system
Meta-heuristics
ISSN:0045-7906
Online Access:Get full text
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Summary:Recommendation systems are decision-support systems used by e-commerce enterprises to evaluate customers’ preferences and suggest items based on their interests. Moreover, they also tackle the problem of information overload. Multi-criteria recommendation systems differ from standard approaches by using multiple-criterion ratings instead of single-criterion ratings while rating products. Multi-criteria recommendation systems has attracted significant attention in the field of recommendation systems due to the lower accuracy of single-criteria recommendation systems. Furthermore, deep learning models have demonstrated encouraging results in several domains including image processing, computer vision, pattern recognition, and natural language processing. This paper introduces a novel methodology leveraging deep autoencoders optimized using the Non-dominated sorting genetic algorithm (NSGA-II) a meta-heuristic optimization technique to enhance the accuracy of multi-criteria recommendation systems. In the first stage of the proposed method, NSGA-II is employed to optimize the weights of the deep autoencoder for enhancing the fine-tuning of the hyperparameters. Secondly, missing ratings and overall ratings are predicted using autoencoders for more precise top-N recommendations. The model is validated using two real-world multi-criteria datasets Yahoo! Movies and TripAdvisor. Experimental results demonstrate that the model achieves significant improvements in prediction accuracy, with a Mean Absolute Error (MAE) of 0.6012 and 0.7215, and Root Mean Squared Error (RMSE) of 0.6137 and 0.7362 on Yahoo! Movies and TripAdvisor datasets, respectively. These findings indicate that the model outperforms both single-criteria recommendation algorithms and other state-of-the-art multi-criteria recommendation models in accuracy. •A rigorous investigation of deep learning-based multi-criteria recommendation systems.•To measure the effectiveness of the proposed model in predicting the missing ratings in the multi-criteria dataset.•Optimization of autoencoders weights using the meta-heuristic technique NSGA-II.•An integration of these methods is validated based on the Yahoo! movies and TripAdvisor multi-criteria dataset.
ISSN:0045-7906
DOI:10.1016/j.compeleceng.2025.110159