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International Journal of Mathematical, Engineering, Biological and Applied Computing
Mini Review | Open Access | 10.31586/ijmebac.2022.350

Open-Source Datasets for Recommender Systems Analysis

Raja Marappan1,*
1
School of Computing, SASTRA Deemed University, Thanjavur, India
Received May 7, 2022
Revised June 17, 2022
Accepted June 25, 2022
Published June 27, 2022

Abstract

There are different traditional and nontraditional datasets available to investigate the performance of recommender systems. This article focuses on the different datasets required for the investigation of recommender systems.

1. Introduction

The following terms are used to define the recommendation systems: items, user, and ratings as sketched in Table 1 [1, 2, 3, 4, 5].

2. Datasets

This section explores the different datasets required to investigate the recommendation systems. The specification and availability of different datasets are sketched in Table 2 [6, 7, 8, 9].

The comparison of datasets using different metrics – users, items, ratings, density, and rating scale is sketched in Table 3 [10, 11, 12, 13, 14, 15].

3. Conclusions & Future Work

This article explained the datasets required for the investigation of recommender systems. These datasets are also compared using the metrics such as users, items, ratings, density, and rating scale. The recommender systems can be developed using several soft computing models in the future [16, 17, 18, 19, 20].

Table 1
Terms for recommendation systems
Table 2
Datasets – specification and availability.
Table 3
Performance comparison of datasets using different metrics

References

  1. G. Adomavicius, A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions IEEE Trans. Knowl. Data Eng. (2005), 10.1109/TKDE.2005.99[CrossRef]
  2. J. Chen, X. Wang, S. Zhao, F. Qian, Y. Zhang. Deep attention user-based collaborative filtering for recommendation Neurocomputing, 383 (2020), 10.1016/j.neucom.2019.09.050[CrossRef]
  3. A. Da’u, N. Salim, I. Rabiu, A. Osman. Recommendation system exploiting aspect-based opinion mining with deep learning method. Inf. Sci., 512 (2020), 10.1016/j.ins.2019.10.038[CrossRef]
  4. Lu J., Zhang Q., Zhang G. Recommender Systems: Advanced Developments World Scientific (2020)[CrossRef]
  5. Zhang S., Yao L., Sun A., Tay Y. Deep learning based recommender system: A survey and new perspectives ACM Comput. Surv. (2019)[CrossRef]
  6. Zhongying Zhao, Xuejian Zhang, Hui Zhou, Chao Li, Maoguo Gong, Yongqing Wang Hetnerec: heterogeneous network embedding based recommendation Knowl. Base Syst., 204 (2020), Article 106218[CrossRef]
  7. Liao W., Zhang Q., Yuan B., Zhang G., Lu J. Heterogeneous multidomain recommender system through adversarial learning IEEE Trans. Neural Netw. Learn. Syst. (2022)[CrossRef] [PubMed]
  8. Zhang Q., Liao W., Zhang G., Yuan B., Lu J. A deep dual adversarial network for cross-domain recommendation IEEE Trans. Knowl. Data Eng. (2021)[CrossRef]
  9. Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He A Survey on Knowledge Graph-Based Recommender Systems IEEE Transactions on Knowledge and Data Engineering (2020)
  10. Zhang Y., Chen X. Explainable recommendation: A survey and new perspectives (2020) arXiv preprint arXiv:1804.11192[CrossRef]
  11. Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets. Mathematics 2020, 8, 1106. https://doi.org/10.3390/math8071106[CrossRef]
  12. Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications. Mathematics 2021, 9, 197. https://doi.org/10.3390/math9020197[CrossRef]
  13. Marappan, R. (2022). Classification and Analysis of Recommender Systems. International Journal of Mathematical, Engineering, Biological and Applied Computing, 1(1), 17–21. DOI: 10.31586/ijmebac.2022.331[CrossRef]
  14. Marappan, R., & Bhaskaran, S. (2022). Movie Recommendation System Modeling Using Machine Learning. International Journal of Mathematical, Engineering, Biological and Applied Computing 2022, 1(1), 12-16. DOI: 10.31586/ijmebac.2022.291[CrossRef]
  15. Marappan, R., & Bhaskaran, S. (2022). Analysis of Network Modeling for Real-world Recommender Systems. International Journal of Mathematical, Engineering, Biological and Applied Computing, 1(1), 1–7. DOI: 10.31586/ijmebac.2022.283[CrossRef]
  16. Marappan, R.; Sethumadhavan, G. Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem. Mathematics 2020, 8, 303. https://doi.org/10.3390/math8030303[CrossRef]
  17. Marappan, R., Sethumadhavan, G. Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J Sci Eng 43, 525–542 (2018). https://doi.org/10.1007/s13369-017-2686-9[CrossRef]
  18. Raja Marappan: A New Multi-Objective Optimization in Solving Graph Coloring and Wireless Networks Channels Allocation Problems. Int. J. Advanced Networking and Applications Volume: 13 Issue: 02 Pages: 4891-4895 (2021)[CrossRef]
  19. R. Marappan and G. Sethumadhavan, "A New Genetic Algorithm for Graph Coloring," 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 49-54, doi: 10.1109/CIMSim.2013.17.[CrossRef]
  20. Raja Marappan, S. Bhaskaran. (2022). Analysis of Recent Trends in E-Learning Personalization Techniques. The Educational Review, USA, 6(5), 167-170. DOI: http://dx.doi.org/10.26855/er.2022.05.003

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© 2024 by author and Scientific Publications. This is an open access article and the related PDF distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Marappan, R. (2022). Open-Source Datasets for Recommender Systems Analysis. International Journal of Mathematical, Engineering, Biological and Applied Computing, 1(2), 49–51. Retrieved from https://www.scipublications.com/journal/index.php/ijmebac/article/view/350
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  1. G. Adomavicius, A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions IEEE Trans. Knowl. Data Eng. (2005), 10.1109/TKDE.2005.99[CrossRef]
  2. J. Chen, X. Wang, S. Zhao, F. Qian, Y. Zhang. Deep attention user-based collaborative filtering for recommendation Neurocomputing, 383 (2020), 10.1016/j.neucom.2019.09.050[CrossRef]
  3. A. Da’u, N. Salim, I. Rabiu, A. Osman. Recommendation system exploiting aspect-based opinion mining with deep learning method. Inf. Sci., 512 (2020), 10.1016/j.ins.2019.10.038[CrossRef]
  4. Lu J., Zhang Q., Zhang G. Recommender Systems: Advanced Developments World Scientific (2020)[CrossRef]
  5. Zhang S., Yao L., Sun A., Tay Y. Deep learning based recommender system: A survey and new perspectives ACM Comput. Surv. (2019)[CrossRef]
  6. Zhongying Zhao, Xuejian Zhang, Hui Zhou, Chao Li, Maoguo Gong, Yongqing Wang Hetnerec: heterogeneous network embedding based recommendation Knowl. Base Syst., 204 (2020), Article 106218[CrossRef]
  7. Liao W., Zhang Q., Yuan B., Zhang G., Lu J. Heterogeneous multidomain recommender system through adversarial learning IEEE Trans. Neural Netw. Learn. Syst. (2022)[CrossRef] [PubMed]
  8. Zhang Q., Liao W., Zhang G., Yuan B., Lu J. A deep dual adversarial network for cross-domain recommendation IEEE Trans. Knowl. Data Eng. (2021)[CrossRef]
  9. Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He A Survey on Knowledge Graph-Based Recommender Systems IEEE Transactions on Knowledge and Data Engineering (2020)
  10. Zhang Y., Chen X. Explainable recommendation: A survey and new perspectives (2020) arXiv preprint arXiv:1804.11192[CrossRef]
  11. Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets. Mathematics 2020, 8, 1106. https://doi.org/10.3390/math8071106[CrossRef]
  12. Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications. Mathematics 2021, 9, 197. https://doi.org/10.3390/math9020197[CrossRef]
  13. Marappan, R. (2022). Classification and Analysis of Recommender Systems. International Journal of Mathematical, Engineering, Biological and Applied Computing, 1(1), 17–21. DOI: 10.31586/ijmebac.2022.331[CrossRef]
  14. Marappan, R., & Bhaskaran, S. (2022). Movie Recommendation System Modeling Using Machine Learning. International Journal of Mathematical, Engineering, Biological and Applied Computing 2022, 1(1), 12-16. DOI: 10.31586/ijmebac.2022.291[CrossRef]
  15. Marappan, R., & Bhaskaran, S. (2022). Analysis of Network Modeling for Real-world Recommender Systems. International Journal of Mathematical, Engineering, Biological and Applied Computing, 1(1), 1–7. DOI: 10.31586/ijmebac.2022.283[CrossRef]
  16. Marappan, R.; Sethumadhavan, G. Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem. Mathematics 2020, 8, 303. https://doi.org/10.3390/math8030303[CrossRef]
  17. Marappan, R., Sethumadhavan, G. Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J Sci Eng 43, 525–542 (2018). https://doi.org/10.1007/s13369-017-2686-9[CrossRef]
  18. Raja Marappan: A New Multi-Objective Optimization in Solving Graph Coloring and Wireless Networks Channels Allocation Problems. Int. J. Advanced Networking and Applications Volume: 13 Issue: 02 Pages: 4891-4895 (2021)[CrossRef]
  19. R. Marappan and G. Sethumadhavan, "A New Genetic Algorithm for Graph Coloring," 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 49-54, doi: 10.1109/CIMSim.2013.17.[CrossRef]
  20. Raja Marappan, S. Bhaskaran. (2022). Analysis of Recent Trends in E-Learning Personalization Techniques. The Educational Review, USA, 6(5), 167-170. DOI: http://dx.doi.org/10.26855/er.2022.05.003

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