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Performance analysis of three recommendation algorithms on Amazon datasets
- 1 Southern University of Science and Technology
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Abstract
Recommendation systems play a crucial role in enhancing user satisfaction and driving sales for businesses. They are essential in today’s marketplaces, as they are able to suggest products and services that may interest a particular individual based on their past purchases. In this paper, empirical research is conducted on three distinct subsets of the Amazon dataset, namely Sports & Outdoors, Movies & TV, and Video Games, to comprehensively evaluate the performance of three distinct recommendation methods based on deep learning algorithms. These methods include the dot product method, (an updated version of the singular value decomposition algorithm), the neural network method, and the neural collaborative filtering model with natural language processing method. The results of this study reveal that deep learning-based recommendation systems can achieve more accurate results compared to traditional recommendation systems for three types of products. The implementation of these methods on Amazon’s dataset can help improve sales by correctly identifying customers’ interests and suggesting relevant items.
Keywords
Recommendation System, Machine Learning, Neural Network
[1]. Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics, 11(1), 141.
[2]. Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian informatics journal, 16(3), 261-273.
[3]. Jin, W., Mao, H., Li, Z., Jiang, H., Luo, C., Wen, H., et al. (2023). Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation. arXiv preprint arXiv:2307.09688.
[4]. Wei, Y., Ma, H., Zhang, R., Li, Z., & Chang, L. (2021). Exploring implicit relationships in social network for recommendation systems. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 386-397.
[5]. Recommender System Using Amazon Reviews, URL: https://www.kaggle.com/code/saurav9786/recommender-system-using-amazon-reviews. Last Accessed: 2023/09/10
[6]. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
[7]. Xue, H. J., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017). Deep matrix factorization models for recommender systems. In IJCAI, 17, 3203-3209.
[8]. Wu, S., Sun, F., Zhang, W., Xie, X., & Cui, B. (2022). Graph neural networks in recommender systems: a survey. ACM Computing Surveys, 55(5), 1-37.
[9]. Paradarami, T. K., Bastian, N. D., & Wightman, J. L. (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300-313.
[10]. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web, 173-182.
Cite this article
Meng,Y. (2024). Performance analysis of three recommendation algorithms on Amazon datasets. Applied and Computational Engineering,51,26-32.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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