The week's pick
Reseach Article
Comparative Analysis of Recommendation System Algorithms for Building Recommendation System as a Service
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 17 |
| Year of Publication: 2023 |
| Authors: Suyash Nehete, Riddhi Dhage, Sarvesh Hon, Tanuja Patankar, Laxmi Kale |
10.5120/ijca2023922881
|
Suyash Nehete, Riddhi Dhage, Sarvesh Hon, Tanuja Patankar, Laxmi Kale . Comparative Analysis of Recommendation System Algorithms for Building Recommendation System as a Service. International Journal of Computer Applications. 185, 17 ( Jun 2023), 24-28. DOI=10.5120/ijca2023922881
Abstract
In the modern world of online enterprises and e-commerce, there is a tremendous need to engage users by delivering relevant and interesting content. Recommendation systems play a crucial role in providing personalized content recommendations to users, which can improve customer interest, purchase rates, and, ultimately, company profits. This paper aims to categorize and explore different types of recommendation systems, such as collaborative and content-based filtering, and examine various methods and algorithms that can be used for implementing these systems. The accuracy of these algorithms is evaluated using an e-commerce dataset. Experimental results revealed that SVD++ achieved the best and lowest RMSE (Root Mean Square Error) with the parameters 'n epochs': 25, 'reg all': 0.4, and 'lr all': 0.01, while SVD had the second best RMSE with the parameters 'n epochs': 20, 'reg all': 0.2, and 'lr all': 0.005. Comparison between SVD and SVDpp revealed slight differences in RMSE and MAE values, but SVD had significantly shorter Fit Time and Test Time (12 times less) compared to SVDpp. Based on the research and experimental results, SVDpp performed the best in terms of RMSE among the Matrix Factorization Based Algorithms, and KNNWithMeans showed promising results in RMSE among the Collaborative Filtering Algorithms.
References
- Hafed Zarzour, Ziad Al-Sharif , Mahmoud Al-Ayyoub, Yaser Jararwe). “A New Collaborative Filtering Recommendation Algorithm Based on Dimensionality Reduction and Clustering Techniques.”. 2018 9th International Conference on Information and Communication Systems (ICICS)
- Bin Li, Sailuo Wan, Hua Xia and Fengshou Qian. “The Research for Recommendation System Based on Improved KNN Algorithm”. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)
- BABAK MALEKI SHOJA, AND NASSEH TABRIZI. “Customer Reviews Analysis with Deep Neural Networks for E-Commerce Recommender Systems”. Citation information: DOI 10.1109/ACCESS.2019.2937518, IEEE Access
- Li H aihan, Qi Guanglei, He N ana, Dong X inri. “Shopping Recommendation System Design Based On Deep Learning”. 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing (ICSP 2021)
- Kyle Ong, Su-Cheng Haw, Kok-Why Ng. “Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends”. CIIS’19, November, 2019, Bangkok, Thailand
- Rohit Dwivedi, Abhineet Anand, Prashant Johri. “ Product Based Recommendation System On Amazon Data”. Researchgate publication 352815845
- Hyeyoung Ko , Suyeon Lee, Yoonseo Park and Anna Choi. “A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields”. Electronics 2022, 11(1), 141; https://doi.org/10.3390/electronics11010141
- Beel, J., Gipp, B., Langer, S. et al. “Research-paper recommender systems: a literature survey”. Int J Digit Libr 17, 305–338 (2016). https://doi.org/10.1007/s00799-015-0156-0
- Cleomar Valois B. JrMarcius Armada de Oliveira. “Recommender systems in social networks”. JISTEM J.Inf.Syst. Technol. Manag. 8 (3) • Dec 2011 • https://doi.org/10.4301/S1807-17752011000300009
- LIU Liling. “Summary of recommendation system development”. 2019 J. Phys.: Conf. Ser. 1187 052044
Index Terms
Keywords