magazinelogo

The Educational Review, USA

ISSN Print: 2575-7938 Downloads: 443553 Total View: 4783685
Frequency: monthly ISSN Online: 2575-7946 CODEN: TERUBB
Email: edu@hillpublisher.com
Article Open Access http://dx.doi.org/10.26855/er.2022.08.009

Analysis of Collaborative, Content & Session Based and Multi-Criteria Recommendation Systems

S. Bhaskaran, Raja Marappan*

School of Computing, SASTRA Deemed University, Thanjavur, India.

*Corresponding author: Raja Marappan

Published: August 22,2022

Abstract

The recommendation systems are performing the role of information filtering in different scenarios. To provide a better recommendation, different approximation methods and soft computing strategies such as machine learning and evolutionary computing are applied. The recommendation systems fulfil the requirements of the users on time. Concerning organizations, the company like to keep their users long on the platforms to maximize the profit. Better recommendations are expected to generate positive feedback for both users and organizations. The recommendation systems utilize approaches like collaborative filtering, knowledge-based systems, and content-based filtering. Most of the common recommender systems nowadays include video and music, online stores, web content, online dating, restaurants, and social media recommendations. These systems use inputs like music, news, books, search queries, etc. The recommendation systems have also been developed for experts, research articles, collaborators, and online commercial services. This research explores the analysis of the collaborative, content & session based, and multi criteria recommendation systems.

References

Bhaskaran, S., Marappan, R., Santhi, B. (2020). 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.

Bhaskaran, S., Marappan, R., Santhi, B. (2021). 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.

Bi, X., Qu, A., Wang, J., Shen, Xiaotong. (2017). A group-specific recommender system. Journal of the American Statistical Association, 112 (519): 1344-1353. doi:10.1080/01621459.2016.1219261.

Cañamares, R., Castells, P., Moffat, A. (March 2020). “Offline Evaluation Options for Recommender Systems” (PDF). Information Retrieval. Springer, 23(4): 387-410. doi:10.1007/s10791-020-09371-3.

Francesco Ricci and Lior Rokach and Bracha Shapira. (2011). Introduction to Recommender Systems Handbook, Recommender Systems Handbook, Springer, 2011, pp. 1-35.

Marappan, R. and 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.

Marappan, R. and Sethumadhavan, G. (2021). Solving Graph Coloring Problem Using Divide and Conquer-Based Turbulent Particle Swarm Optimization. Arab J SciEng, (2021). https://doi.org/10.1007/s13369-021-06323-x.

Marappan, R. and Sethumadhavan, G. (2018). Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J SciEng, 43, 525-542 (2018). https://doi.org/10.1007/s13369-017-2686-9.

Marappan, R. and Sethumadhavan, G. (2020). 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.

Prem Melville and Vikas Sindhwani. (2010). Recommender Systems, Encyclopedia of Machine Learning, 2010.

Raja Marappan and S. Bhaskaran. (2022a). 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.

Raja Marappan and S. Bhaskaran. (2022b). Datasets Finders and Best Public Datasets for Machine Learning and Data Science Applications. COJ Rob Artificial Intel., 2(1). COJRA. 000530. 2022.

Rubens, N., Elahi, M., Sugiyama, M., Kaplan, D. (2016). “Active Learning in Recommender Systems”. In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6_24. ISBN 978-1-4899-7637-6.

Waila, P., Singh, V., and Singh, M. (26 April 2016). “A Scientometric Analysis of Research in Recommender Systems” (PDF). Journal of Scientometric Research, 5: 71-84. doi:10.5530/jscires.5.1.10.

Xin, X., Karatzoglou, A., Arapakis, I., and Jose, J. (2020). “Self-Supervised Reinforcement Learning for Recommender Systems”. arXiv:2006.05779.

Zou, Lixin, Xia, L.,Ding, Zhuoye, Song, Jiaxing, Liu, Weidong, Yin, Dawei. (2019). “Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems”. KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '19: 2810–2818. arXiv:1902.05570. doi:10.1145/3292500.3330668. ISBN 9781450362016.

How to cite this paper

Analysis of Collaborative, Content & Session Based and Multi-Criteria Recommendation Systems

How to cite this paper: S. Bhaskaran, Raja Marappan. (2022). Analysis of Collaborative, Content & Session Based and Multi-Criteria Recommendation Systems. The Educational Review, USA6(8), 387-390.

DOI: http://dx.doi.org/10.26855/er.2022.08.009