Enhancing Book Recommendation Accuracy through User Rating Analysis and Collaborative Filtering Techniques An Empirical Analysis
Main Article Content
Keywords
Collaborative Filtering, k-Nearest Neighbors (k-NN), Matrix Factorization, RapidMiner Studio,, Data Mining Techniques
Abstract
Since online electronic books have become popular, book recommendation systems have been invented and challenged to handle the high demand from users in the digital era. This study aimed to develop and evaluate a book recommendation model using data mining techniques through RapidMiner Studio. The datasets used were comprised of 981,756 user ratings. Before conducting the data analytics, the data was pre-processed to eliminate duplicates and retain only the highest ratings. Collaborative Filtering (CF) techniques, particularly k-Nearest Neighbours (k-NN) and Matrix Factorization (KF), were employed to elicit insightful information for development and to highlight their capabilities in handling enormous datasets. Furthermore, statistical analysis, visualization, elementary modelling, and model combinations were investigated to compare their performance.
To reinforce creditability, modelling techniques and parameter adjustments were integrated to optimize the performance of the algorithms, since the results indicated that different model settings and data partitions impacted the effectiveness of the recommendation system. Additionally, these results demonstrated the potential of hybrid models in improving the accuracy and efficiency of recommendation systems and highlighted the trade-off between algorithmic approaches and dataset characteristics that interplay in optimizing the performance of recommendation systems.
Downloads
Download data is not yet available.
References
Al-Ghuribi, S. M., Noah, S. A., & Mohammed, M. A. (2023). An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets. PeerJ Computer Science, 9, e1525. https://doi.org/10.7717/peerj-cs.1525
Al-Hagery, M. A. (2020). A novel based-approach composed of clustering algorithm & cosine similarity for products recommendation. International Journal of Education and Information Technologies, 14, 133–141. https://doi.org/10.46300/9109.2020.14.16
Ang, J. Y., & Haw, S. C. (2021). Comparative analysis of techniques used in book-based recommender system. In Proceedings of the 5th International Conference on Digital Technology in Education (pp. 87–92). ACM. https://doi.org/10.1145/3488466.3488475
Bharathi, Y. D. (2019). Recommendation system for video streaming websites based on user feedback. International Journal of Engineering and Advanced Technology, 8(6), 1317–1320. https://doi.org/10.35940/ijeat.F8516.088619
Bharathipriya, C., Swathi, B., & Jency, X. F. (2020). Product recommendation framework based on customer review using collaborative filtering techniques. Journal of Mechanics of Continua and Mathematical Sciences, (Special Issue 7), 58–71. https://doi.org/10.26782/jmcms.spl.7/2020.02.00004
Bošnjak, M., Antulov-Fantulin, N., Šmuc, T., & Gamberger, D. (2011, June). Constructing recommender systems workflow templates in RapidMiner. In Proceedings of the 2nd RapidMiner Community Meeting and Conference (pp. 101–112). Shaker Verlag. https://www.shaker.eu/Online-Gesamtkatalog-Download/2024.08.10-08.53.51-66.249.66.88-radE74BB.tmp/3-8440-0093-3_INH.PDF
Chaturvedi, A., Subramanian, S. P., Kumar, A., & Mishra, B. (2023). Synergizing collaborative filtering and popularity scores in a machine learning approach for content suggestions. In 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 442–449). IEEE. https://doi.org/10.1109/ICECA58529.2023.10395086
Devika, P. V., Jyothisree, K., Rahul, P. V., Arjun, S., & Narayanan, J. (2021, July). Book recommendation system. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9579647
Foxtrot. (2024). Goodbooks-10k. Kaggle. https://www.kaggle.com/zygmunt/goodbooks-10k
Hikmatyar, M. (2020). Book recommendation system development using user-based collaborative filtering. Journal of Physics: Conference Series, 1477(3), 032024. https://doi.org/10.1088/1742-6596/1477/3/032024
Ifada, N., Susanto, L. R., Saputro, P. A., Nugraha, A. P., Muflikhah, L., Fauzi, M. A., & Adinugroho, S. (2019). Enhancing the performance of library book recommendation system by employing the probabilistic-keyword model on a collaborative filtering approach. Procedia Computer Science, 157, 345–352. https://doi.org/10.1016/j.procs.2019.08.176
Isinkaye, F. O. (2023). Harnessing Item Features to Enhance Recommendation Quality of Collaborative Filtering. Journal of Applied Intelligent System, 8(2),162–172. http://dx.doi.org/10.33633/jais.v8i2.7915
Jain, A., & Vishwakarma, S. K. (2017). Collaborative filtering for movie recommendation using RapidMiner. International Journal of Computer Applications, 169(5), 29–33. https://www.ijcaonline.org/archives/volume169/number6/jain-2017-ijca-914771.pdf
Khan, B. M., Khan, M. T., Malik, A. B., & Ahmad, K. S. (2017). Collaborative filtering based online recommendation systems: A survey. In 2017 International Conference on Information and Communication Technologies (ICICT) (pp. 125–130). IEEE. https://doi.org/10.1109/ICICT.2017.8320176
Kumar, P., Gupta, M. K., Rao, C. R. S., Bhavsingh, M., & Srilakshmi, M. (2023). A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 184–192. https://doi.org/10.17762/ijritcc.v11i3s.6180
Li, Z., Xiao, Q., Liu, Y., Lin, Y., & Luo, F. (2023). BookGPT: A general framework for book recommendation empowered by large language model. Electronics, 12(22), 4654. https://doi.org/10.3390/electronics12224654
Mankar, K., Wankhade, P., Bahurupi, S., Jirapure, S., Deshmukh, A., & Thakare, S. (2023). Web based book recommendation system using collaborative filtering. In 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1–6). IEEE. https://doi.org/10.1109/ESCI56872.2023.10099750
Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book recommendation system through content-based and collaborative filtering method. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) (pp. 47–52). IEEE. https://doi.org/10.1109/SAPIENCE.2016.7684166
Mihelčić, M., Antulov-Fantulin, N., Bošnjak, M., & Šmuc, T. (2012). Extending RapidMiner with recommender systems algorithms. Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012). http://bib.irb.hr/datoteka/596976.rcomm2012_recommenders.pdf
Munaji, A. A., & Emanuel, A. W. (2021). Restaurant recommendation system based on user ratings with collaborative filtering. IOP Conference Series: Materials Science and Engineering, 1077(1). https://doi.org/012008. 10.1088/1757-899X/1077/1/012026
Muneer, M., Khan, M. A., Manzoor, S., Naeem, M., & Saeed, H. (2022). Tour spot recommendation system via content-based filtering. In 2022 16th International Conference on Open Source Systems and Technologies (ICOSST) (pp. 1–6). IEEE. https://doi.org/10.1109/ICOSST57195.2022.10016820
Ngoendee, W., & Charoenruengkit, W. (2020). Book recommendation with data mining using RapidMiner [Master’s project, Srinakharinwirot University]. Institutional Repository. http://ir-ithesis.swu.ac.th/dspace/bitstream/123456789/1232/1/gs591130027.pdf
Pasricha, H., & Solanki, S. (2019). A new approach for book recommendation using opinion leader mining. In Proceedings of the International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT 2018) (pp. 501–515). Springer Singapore. https://doi.org/10.1007/978-981-13-5802-9_46
Patel, D., & Patidar, H. (2018). Hybrid recommendation solution for online book portal. International Journal for Research in Applied Science and Engineering Technology, 6(5), 1367–1373. http://doi.org/10.22214/ijraset.2018.5225
Raghavendra, C. K., & Srikantaiah, K. C. (2022). Switching hybrid model for personalized recommendations by combining users demographic information. Journal of Theoretical and Applied Information Technology, 100(3), 825–835. http://www.jatit.org/volumes/Vol100No3/20Vol100No3.pdf
Rajalakshmi, S., Indumathi, G., Elias, A., & Priya, G. S. (2024). Personalized Online Book Recommendation System Using Hybrid Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 39–46. https://www.ijisae.org/index.php/IJISAE/article/view/4712
Rana, A., & Deeba, K. (2019). Online book recommendation system using collaborative filtering (with Jaccard similarity). Journal of Physics: Conference Series, 1362(1). https://doi.org/012130. 10.1088/1742-6596/1362/1/012130
Rao, B., Bhargava, P., Panchal, D., Rane, S., & Lalwani, P. (2021). Book recommendation system with relevant text audiobook generation. International Journal of Creative Research Thoughts (IJCRT), 42(1), 398–404. https://ijcrt.org/papers/IJCRT2107170.pdf
Saravanapriya, M., Senthilkumar, R., & Saktheeswaran, J. (2022, July). Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining. Journal of Scientific and Industrial Research (JSIR), 81, 785–797. http://op.niscair.res.in/index.php/JSIR/article/download/46261/465480990
Sarimehmetoğlu, B., & Erdem, H. (2023). Extracting Book Titles From Book Recommendation Videos Using a Deep Learning Approach. MANAS Journal of Engineering, 11(2), 229–234. https://doi.org/10.51354/mjen.1369636
Sarma, D., Mittra, T., & Shahadat, M. (2021). Personalized book recommendation system using machine learning algorithm. International Journal of Advanced Computer Science and Applications, 12(6), 212–219. https://doi.org/10.14569/IJACSA.2021.0120126
Singh, K. K., & Banerjee, I. (2023). Integrated personalized book recommendation using social media analysis. Parikalpana KIIT Journal of Management, 19(1), 106–123. http://doi.org/10.23862/kiit-parikalpana/2023/v19/i1/220834
Tang, Z., & Wen, Z. (2015). Recommendation system based on collaborative filtering in RapidMiner. Computer modelling & new technologies, 18(11), 1004–1008. http://www.cmnt.lv/upload-files/ns_19art162.pdf
Tian, Y., Zheng, B., Wang, Y., Zhang, Y., & Wu, Q. (2019). College library personalized recommendation system based on hybrid recommendation algorithm. Procedia CIRP, 83, 490–494. https://doi.org/10.1016/j.procir.2019.04.126
You, Z., Hu, H., Wang, Y., Xue, J., & Yi, X. (2023). Improved Hybrid Collaborative Filtering Algorithm Based on Spark Platform. Wuhan University Journal of Natural Sciences, 28(5), 451–460. https://doi.org/10.1051/wujns/2023285451
Zhao, W., Tian, H., Wu, Y., Cui, Z., & Feng, T. (2022). A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data. International Journal of Computational Intelligence Systems, 15, a15 https://doi.org/10.1007/s44196-022-00068-7
Al-Hagery, M. A. (2020). A novel based-approach composed of clustering algorithm & cosine similarity for products recommendation. International Journal of Education and Information Technologies, 14, 133–141. https://doi.org/10.46300/9109.2020.14.16
Ang, J. Y., & Haw, S. C. (2021). Comparative analysis of techniques used in book-based recommender system. In Proceedings of the 5th International Conference on Digital Technology in Education (pp. 87–92). ACM. https://doi.org/10.1145/3488466.3488475
Bharathi, Y. D. (2019). Recommendation system for video streaming websites based on user feedback. International Journal of Engineering and Advanced Technology, 8(6), 1317–1320. https://doi.org/10.35940/ijeat.F8516.088619
Bharathipriya, C., Swathi, B., & Jency, X. F. (2020). Product recommendation framework based on customer review using collaborative filtering techniques. Journal of Mechanics of Continua and Mathematical Sciences, (Special Issue 7), 58–71. https://doi.org/10.26782/jmcms.spl.7/2020.02.00004
Bošnjak, M., Antulov-Fantulin, N., Šmuc, T., & Gamberger, D. (2011, June). Constructing recommender systems workflow templates in RapidMiner. In Proceedings of the 2nd RapidMiner Community Meeting and Conference (pp. 101–112). Shaker Verlag. https://www.shaker.eu/Online-Gesamtkatalog-Download/2024.08.10-08.53.51-66.249.66.88-radE74BB.tmp/3-8440-0093-3_INH.PDF
Chaturvedi, A., Subramanian, S. P., Kumar, A., & Mishra, B. (2023). Synergizing collaborative filtering and popularity scores in a machine learning approach for content suggestions. In 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 442–449). IEEE. https://doi.org/10.1109/ICECA58529.2023.10395086
Devika, P. V., Jyothisree, K., Rahul, P. V., Arjun, S., & Narayanan, J. (2021, July). Book recommendation system. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9579647
Foxtrot. (2024). Goodbooks-10k. Kaggle. https://www.kaggle.com/zygmunt/goodbooks-10k
Hikmatyar, M. (2020). Book recommendation system development using user-based collaborative filtering. Journal of Physics: Conference Series, 1477(3), 032024. https://doi.org/10.1088/1742-6596/1477/3/032024
Ifada, N., Susanto, L. R., Saputro, P. A., Nugraha, A. P., Muflikhah, L., Fauzi, M. A., & Adinugroho, S. (2019). Enhancing the performance of library book recommendation system by employing the probabilistic-keyword model on a collaborative filtering approach. Procedia Computer Science, 157, 345–352. https://doi.org/10.1016/j.procs.2019.08.176
Isinkaye, F. O. (2023). Harnessing Item Features to Enhance Recommendation Quality of Collaborative Filtering. Journal of Applied Intelligent System, 8(2),162–172. http://dx.doi.org/10.33633/jais.v8i2.7915
Jain, A., & Vishwakarma, S. K. (2017). Collaborative filtering for movie recommendation using RapidMiner. International Journal of Computer Applications, 169(5), 29–33. https://www.ijcaonline.org/archives/volume169/number6/jain-2017-ijca-914771.pdf
Khan, B. M., Khan, M. T., Malik, A. B., & Ahmad, K. S. (2017). Collaborative filtering based online recommendation systems: A survey. In 2017 International Conference on Information and Communication Technologies (ICICT) (pp. 125–130). IEEE. https://doi.org/10.1109/ICICT.2017.8320176
Kumar, P., Gupta, M. K., Rao, C. R. S., Bhavsingh, M., & Srilakshmi, M. (2023). A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 184–192. https://doi.org/10.17762/ijritcc.v11i3s.6180
Li, Z., Xiao, Q., Liu, Y., Lin, Y., & Luo, F. (2023). BookGPT: A general framework for book recommendation empowered by large language model. Electronics, 12(22), 4654. https://doi.org/10.3390/electronics12224654
Mankar, K., Wankhade, P., Bahurupi, S., Jirapure, S., Deshmukh, A., & Thakare, S. (2023). Web based book recommendation system using collaborative filtering. In 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1–6). IEEE. https://doi.org/10.1109/ESCI56872.2023.10099750
Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book recommendation system through content-based and collaborative filtering method. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) (pp. 47–52). IEEE. https://doi.org/10.1109/SAPIENCE.2016.7684166
Mihelčić, M., Antulov-Fantulin, N., Bošnjak, M., & Šmuc, T. (2012). Extending RapidMiner with recommender systems algorithms. Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012). http://bib.irb.hr/datoteka/596976.rcomm2012_recommenders.pdf
Munaji, A. A., & Emanuel, A. W. (2021). Restaurant recommendation system based on user ratings with collaborative filtering. IOP Conference Series: Materials Science and Engineering, 1077(1). https://doi.org/012008. 10.1088/1757-899X/1077/1/012026
Muneer, M., Khan, M. A., Manzoor, S., Naeem, M., & Saeed, H. (2022). Tour spot recommendation system via content-based filtering. In 2022 16th International Conference on Open Source Systems and Technologies (ICOSST) (pp. 1–6). IEEE. https://doi.org/10.1109/ICOSST57195.2022.10016820
Ngoendee, W., & Charoenruengkit, W. (2020). Book recommendation with data mining using RapidMiner [Master’s project, Srinakharinwirot University]. Institutional Repository. http://ir-ithesis.swu.ac.th/dspace/bitstream/123456789/1232/1/gs591130027.pdf
Pasricha, H., & Solanki, S. (2019). A new approach for book recommendation using opinion leader mining. In Proceedings of the International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT 2018) (pp. 501–515). Springer Singapore. https://doi.org/10.1007/978-981-13-5802-9_46
Patel, D., & Patidar, H. (2018). Hybrid recommendation solution for online book portal. International Journal for Research in Applied Science and Engineering Technology, 6(5), 1367–1373. http://doi.org/10.22214/ijraset.2018.5225
Raghavendra, C. K., & Srikantaiah, K. C. (2022). Switching hybrid model for personalized recommendations by combining users demographic information. Journal of Theoretical and Applied Information Technology, 100(3), 825–835. http://www.jatit.org/volumes/Vol100No3/20Vol100No3.pdf
Rajalakshmi, S., Indumathi, G., Elias, A., & Priya, G. S. (2024). Personalized Online Book Recommendation System Using Hybrid Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 39–46. https://www.ijisae.org/index.php/IJISAE/article/view/4712
Rana, A., & Deeba, K. (2019). Online book recommendation system using collaborative filtering (with Jaccard similarity). Journal of Physics: Conference Series, 1362(1). https://doi.org/012130. 10.1088/1742-6596/1362/1/012130
Rao, B., Bhargava, P., Panchal, D., Rane, S., & Lalwani, P. (2021). Book recommendation system with relevant text audiobook generation. International Journal of Creative Research Thoughts (IJCRT), 42(1), 398–404. https://ijcrt.org/papers/IJCRT2107170.pdf
Saravanapriya, M., Senthilkumar, R., & Saktheeswaran, J. (2022, July). Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining. Journal of Scientific and Industrial Research (JSIR), 81, 785–797. http://op.niscair.res.in/index.php/JSIR/article/download/46261/465480990
Sarimehmetoğlu, B., & Erdem, H. (2023). Extracting Book Titles From Book Recommendation Videos Using a Deep Learning Approach. MANAS Journal of Engineering, 11(2), 229–234. https://doi.org/10.51354/mjen.1369636
Sarma, D., Mittra, T., & Shahadat, M. (2021). Personalized book recommendation system using machine learning algorithm. International Journal of Advanced Computer Science and Applications, 12(6), 212–219. https://doi.org/10.14569/IJACSA.2021.0120126
Singh, K. K., & Banerjee, I. (2023). Integrated personalized book recommendation using social media analysis. Parikalpana KIIT Journal of Management, 19(1), 106–123. http://doi.org/10.23862/kiit-parikalpana/2023/v19/i1/220834
Tang, Z., & Wen, Z. (2015). Recommendation system based on collaborative filtering in RapidMiner. Computer modelling & new technologies, 18(11), 1004–1008. http://www.cmnt.lv/upload-files/ns_19art162.pdf
Tian, Y., Zheng, B., Wang, Y., Zhang, Y., & Wu, Q. (2019). College library personalized recommendation system based on hybrid recommendation algorithm. Procedia CIRP, 83, 490–494. https://doi.org/10.1016/j.procir.2019.04.126
You, Z., Hu, H., Wang, Y., Xue, J., & Yi, X. (2023). Improved Hybrid Collaborative Filtering Algorithm Based on Spark Platform. Wuhan University Journal of Natural Sciences, 28(5), 451–460. https://doi.org/10.1051/wujns/2023285451
Zhao, W., Tian, H., Wu, Y., Cui, Z., & Feng, T. (2022). A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data. International Journal of Computational Intelligence Systems, 15, a15 https://doi.org/10.1007/s44196-022-00068-7
Article Sidebar
Published
Sep 30, 2024
Article Details
How to Cite
Chongwarin, J., Manorom, P., Chaichuay, V., Boongoen, T., Li, C., & Chansanam, W. (2024). Enhancing Book Recommendation Accuracy through User Rating Analysis and Collaborative Filtering Techniques: An Empirical Analysis. Journal of Telecommunications and the Digital Economy, 12(3), 51–72. https://doi.org/10.18080/jtde.v12n3.976
Issue
Section
Special Issue: Perspectives on Machine Learning
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Telecommunications Association Inc.
