An Intelligent Weighted Recommendation Technique utilizing Ensemble System for Enhanced Prediction Accuracy for better Consumer Decision


  • Analp Pathak, B. K. Sharma


Intelligent Recommendation System, Machine Learning, Multi-nomial Naïve Bayes, Multi-Layer Perceptron, Logistic Regression, and Ensemble Classifiers


A recommendation system can intelligently employ machine learning algorithms to suggest diverse options tailored to user interests based on multiple sources of information. Most recommendation systems heavily rely on the collaborative filtering (CF) approach, where user preference data is amalgamated with that of other users to predict additional items of potential interest to the consumer. In this study, an innovative weighted recommendation system is developed to enhance consumer decision-making using CF. Equations to calculate the weight of both the product and review, as well as the similarity between consumer reviews, are devised in the methodology. The methodology employs machine learning techniques such as Multi-nomial Naïve Bayes (MNB), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) as intelligent ensemble models. Ensemble Classifiers (MNB+MLP+LR) are utilized to implement the methodology's results, aiming for superior outcomes compared to previous studies. The proposed model is trained and tested using an open-source dataset. Numerical analysis of the proposed model demonstrates its superior performance over conventional methods in terms of accuracy (0.952), precision (0.908), recall (0.897), F-measure (0.941), error rate (0.087), and other metrics.


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Cui, Laizhong, Linyong Dong, Xianghua Fu, Zhenkun Wen, Nan Lu, and Guanjing Zhang. "A video recommendation algorithm based on the combination of video content and social network." Concurrency and Computation: Practice and Experience 29, no. 14 (2017): e3900.

Liu, Mengsi, Weike Pan, Miao Liu, Yaofeng Chen, Xiaogang Peng, and Zhong Ming. "Mixed similarity learning for recommendation with implicit feedback." Knowledge-Based Systems 119 (2017): 178-185.

Ding, Linlin, Baishuo Han, Shu Wang, Xiaoguang Li, and Baoyan Song. "User-centered recommendation using us-elm based on dynamic graph model in e-commerce." International Journal of Machine Learning and Cybernetics 10 (2019): 693-703.

Bouras, Christos, and Vassilis Tsogkas. "Improving news articles recommendations via user clustering." International Journal of Machine Learning and Cybernetics 8 (2017): 223-237.

Kim, Byung-Do, and Sun-Ok Kim. "A new recommender system to combine content-based and collaborative filtering systems." Journal of Database Marketing & Customer Strategy Management 8 (2001): 244-252.

Sivaramakrishnan, N., and V. Subramaniyaswamy. "GPU-based Collaborative Filtering Recommendation System using Task parallelism approach." 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on. IEEE, 2018

Deng, Xiaoyi, Fuzhen Zhuang, and Zhiguo Zhu. "Neural variational collaborative filtering with side information for top-K recommendation." International Journal of Machine Learning and Cybernetics 10 (2019): 3273-3284.

He, Xiangnan, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. "Neural collaborative filtering." In Proceedings of the 26th international conference on world wide web, pp. 173-182. 2017.

Wang, Wei, et al. "Trust-enhanced collaborative filtering for personalized point of interests recommendation." IEEE Transactions on Industrial Informatics 16.9 (2019): 6124-6132.

Mohd Sabri, Norlina, and Nurul Azeymasnita Jaffar. "Book recommendation based on collaborative filtering technique." ESTEEM Academic Journal 18 (2022): 92-103.

Han, Di, Yijun Chen, and Shuya Zhang. "Implicit social recommendation algorithm based on multilayer fuzzy perception similarity." International Journal of Machine Learning and Cybernetics 13, no. 2 (2022): 357-369.

Sharma, Sunny, Vijay Rana, and Manisha Malhotra. "Automatic recommendation system based on hybrid filtering algorithm." Education and Information Technologies (2022): 1-16.

Zhao, Yan. "Design of Garment Style Recommendation System Based on Interactive Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (2022).

Tahira, Anum, Walayat Hussain, and Arif Ali. "Based Recommender System for Hedonic and Utilitarian Products in IoT Framework." In IoT as a Service: 7th EAI International Conference, IoTaaS 2021, Sydney, Australia, December 13–14, 2021, Proceedings, pp. 221-232. Cham: Springer International Publishing, 2022.

Chen, Jianrui, Bo Wang, Zhiping Ouyang, and Zhihui Wang. "Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network." International Journal of Machine Learning and Cybernetics 12 (2021): 1097-1113.

Lai, Chin-Hui, Duen-Ren Liu, and Kun-Sin Lien. "A hybrid of XGBoost and aspect-based review mining with attention neural network for user preference prediction." International Journal of Machine Learning and Cybernetics 12 (2021): 1203-1217.

Bi, Jian-Wu, Yang Liu, and Zhi-Ping Fan. "A deep neural networks-based recommendation algorithm using user and item basic data." International Journal of Machine Learning and Cybernetics 11 (2020): 763-777

Abbasi, Fatemeh, and Ameneh Khadivar. "Collaborative Filtering Recommendation System through Sentiment Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 14 (2021): 1843-1853.

Ghosh, Monalisa, and Goutam Sanyal. "An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning." Journal of Big Data 5 (2018): 1-25.

Zhang, Jinghua, Chen Li, Yimin Yin, Jiawei Zhang, and Marcin Grzegorzek. "Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer." Artificial Intelligence Review (2022): 1-58.

Greco, Claudia, Pasquale Pace, Stefano Basagni, and Giancarlo Fortino. "Jamming detection at the edge of drone networks using Multi-layer Perceptrons and Decision Trees." Applied Soft Computing 111 (2021): 107806.

Ribokaitė, Lina. "Outlier detection in multidimensional streaming data." PhD diss., Vilniaus universitetas, 2021.




How to Cite

Analp Pathak. (2024). An Intelligent Weighted Recommendation Technique utilizing Ensemble System for Enhanced Prediction Accuracy for better Consumer Decision. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2516–2530. Retrieved from



Research Article