Personalized Online Book Recommendation System Using Hybrid Machine Learning Techniques

Authors

  • S. Rajalakshmi Associate Professor, Department of Computer Science & Engineering, Sri Venkateswara College of Engineering, Sriperumbudur.
  • G. Indumathi Assistant Professor, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai
  • Arun Elias Professor, Department of CSE, Mahaguru Institute of Technology, Kerala
  • G. Shanmuga Priya Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore
  • Vidhya Muthulakshmi. R Assistant Professor, Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai

Keywords:

Personalized Book, Recommendation System, e-book, Machine Learning, Filtering, Classification

Abstract

Recently, the scientific world has become interested in recommender systems research due to its exponential development. The COVID-19 pandemic has caused an exponential increase in the number of books available online, making it extremely difficult for readers to identify relevant books within the large e-book sector. According to user ratings and interests, personal recommendation systems have developed as an efficient way to search for relevant books. Recommendation systems are robust emerging technologies that aid consumers in finding products that they wish to purchase. Recommendation systems are often implemented to suggest proper goods to end customers. Recently, websites that offer books online contest with one another based on a wide range of criteria. One of the best methods to boost profits and keep customers is a recommendation system. Users are not satisfied with the current systems since they extract unnecessary information from them. To generate highly effective and productive recommendations, this study proposes the Personalized Online Book Recommendation System (PO-BRS), which is based on machine learning techniques. The authors proposed hybrid machine learning approaches that combine two or more algorithms to improve the recommendation system's ability to suggest books based on the interests of the reader. As a result, recommendations based on a specific book are found to be more accurate and profitable than systems that depend on user input.

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References

Sarma, D., Mittra, T., & Hossain, M. S. (2021). Personalized book recommendation system using machine learning algorithm. International Journal of Advanced Computer Science and Applications, 12(1).

Grover, N. (2019). Enabling shift in retail using data: Case of Amazon.

Lee, Y. H., Wei, C. P., Hu, P. J. H., Cheng, T. H., & Lan, C. W. (2020). Small clues tell: a collaborative expansion approach for effective content-based recommendations. Journal of Organizational Computing and Electronic Commerce, 30(2), 111-128.

Zhao, T., Zhang, W., Zhang, Y., Liu, Z., & Chen, X. (2020). Significant spatial patterns from the GCM seasonal forecasts of global precipitation. Hydrology and Earth System Sciences, 24(1), 1-16.

Gazdar, A., & Hidri, L. (2020). A new similarity measure for collaborative filtering based recommender systems. Knowledge-Based Systems, 188, 105058.

Kommineni, M., Alekhya, P., Vyshnavi, T. M., Aparna, V., Swetha, K., & Mounika, V. (2020, January). Machine learning based efficient recommendation system for book selection using user based collaborative filtering algorithm. In 2020 Fourth International Conference on Inventive Systems and Control (ICISC) (pp. 66-71). IEEE.

Gharia, K. N., Desai, P. V., & Gandhi, M. R. (2018). Review paper on novel recommendation. In 2018 second international conference on computing methodologies and communication (iccmc) (pp. 346-348).

Pradeep, I. K., & Bhaskar, M. J. (2018). Comparative analysis of recommender systems and its enhancements. International Journal of Engineering & Technology, 7(3.29), 304-310.

Lu, Z., Dou, Z., Lian, J., Xie, X., & Yang, Q. (2015, February). Content-based collaborative filtering for news topic recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 29, No. 1).

Lakshmi, C. R., Rao, D. T., & Rao, G. S. (2017, September). Fog detection and visibility enhancement under partial machine learning approach. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) (pp. 1192-1194). IEEE.

Okon, E. U., Eke, B. O., & Asagba, P. O. (2018). An improved online book recommender system using collaborative filtering algorithm. International Journal of Computer Applications, 179(46), 41-48.

Jaiswal, A., & Malhotra, R. (2018). Software reliability prediction using machine learning techniques. International Journal of System Assurance Engineering and Management, 9, 230-244.

Cho, E., & Han, M. (2019, April). AI powered book recommendation system. In Proceedings of the 2019 ACM Southeast Conference (pp. 230-232).

Liu, Q., Zhang, X., Zhang, L., & Zhao, Y. (2019). The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: An empirical investigation. Electronic Commerce Research, 19, 521-547.

Alam, M. N., Sarma, D., Lima, F. F., Saha, I., & Hossain, S. (2020, August). Phishing attacks detection using machine learning approach. In 2020 third international conference on smart systems and inventive technology (ICSSIT) (pp. 1173-1179). IEEE.

Kumar, S. G., Sridhar, S. S., Hussain, A., Manikanthan, S. V., & Padmapriya, T. (2022). Personalized web service recommendation through mishmash technique and deep learning model. Multimedia Tools and Applications, 81(7), 9091-9109.

Saha, I., Sarma, D., Chakma, R. J., Alam, M. N., Sultana, A., & Hossain, S. (2020, August). Phishing attacks detection using deep learning approach. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1180-1185). IEEE.

Ahmed, F., Chakma, R. J., Hossain, S., & Sarma, D. (2020, February). A combined belief rule based expert system to predict coronary artery disease. In 2020 international conference on inventive computation technologies (ICICT) (pp. 252-257). IEEE.

Sharma, S. C. M., Mitra, A., & Chakraborty, D. (2020). Concepts of Recommendation System from the Perspective of Machine Learning. Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries, 71-87.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.

Sembium, V., Rastogi, R., Tekumalla, L., & Saroop, A. (2018, April). Bayesian models for product size recommendations. In Proceedings of the 2018 world wide web conference (pp. 679-687).

Raghuwanshi, S. K., & Pateriya, R. K. (2019). Recommendation systems: Techniques, challenges, application, and evaluation. In Soft Computing for Problem Solving: SocProS 2017, Volume 2 (pp. 151-164). Springer Singapore.

Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 29-39.

Rohit, Sabitha, S., & Choudhury, T. (2018). Proposed approach for book recommendation based on user k-NN. In Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016, Volume 2 (pp. 543-558). Springer Singapore.

Muruganandam, S., Salameh, A. A., Pozin, M. A. A., Manikanthan, S. V., & Padmapriya, T. (2023). Sensors and machine learning and AI operation-constrained process control method for sensor-aided industrial internet of things and smart factories. Measurement: Sensors, 25, 100668.

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Published

07.02.2024

How to Cite

Rajalakshmi, S. ., Indumathi, G. ., Elias, A. ., Priya, G. S. ., & Muthulakshmi. R, V. (2024). Personalized Online Book Recommendation System Using Hybrid Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 39–46. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4712

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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