Abstract
E-commerce industry has become more popular over the last 20 years with the increased popularity of the Internet. Thus, the Internet facilitated retail for individuals who are able to order products online that get delivered to their own home. This made users’ experience crucial in e-commerce industry. In order to understand users’ experience and improve it, e-commerce industry has been using business tools such as recommender systems. These systems have a challenge when applied on their own to big datasets. Consequently, this research combined the use of a hybrid recommender system and machine learning analytics for predicting users’ preferences for products and products’ popularity from an e-commerce dataset. It is worth noting that the hybrid recommender system combined both collaborative and content-based filtering. In this respect, both random forest and alternative least square models were applied to LightFM and Sparks recommender systems, respectively. Hyperparameter tuning was crucial for optimizing random forest model that gave an AUC in the range of 0.6–0.96. In addition, alternative least square model was evaluated by the root mean square error score that was 0.259. Hence, random forest model applied to hybrid recommender system outperformed the alternative least square-based model. The findings showed to be useful for large e-commerce datasets where the hybrid model showed that it can handle large volumes of datasets without affecting performance accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng, Q., Yang, C., Li, S., Li, F.: E-Commerce Strategy. Springer (2014)
Hussien, F.T., Rahma, A.M.S., Abdulwahab, H.B.: An e‐commerce recommendation system based on dynamic analysis of customer behavior. Sustainability (Switzerland) 13(19) (2021). https://doi.org/10.3390/su131910786
Damanpour, F., Damanpour, J.A.: E-business e-commerce evolution: perspective and strategy. Manag. Financ. 27(7), 16–33 (2001)
Tyagi, R., Jawdekar, A.: An advanced recommendation system for E-commerce users. In: 2016 Symposium on Colossal Data Analysis and Networking, CDAN, pp. 2–7 (2016). https://doi.org/10.1109/CDAN.2016.7570876
Elgayed, M., Taher Attia, S.: Online impulsive buying behavior: the mediating effect of browsing on Egyptian consumers. J. Bus. Manage. Sci. 11(1), 34–45 (2023). https://doi.org/10.12691/jbms-11-1-3
Jung, J., Matsuba, Y., Mallipeddi, R., Funaya, H., Ikeda, K., Lee, M.: Evolutionary programming based recommendation system for online shopping. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA (2013). https://doi.org/10.1109/APSIPA.2013.6694236
Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., Wang, X.: A trust-based collaborative filtering algorithm for E-commerce recommendation system. J. Ambient. Intell. Humaniz. Comput. 10(8), 3023–3034 (2019). https://doi.org/10.1007/s12652-018-0928-7
Xue, W., Xiao, B., Mu, L.: Intelligent mining on purchase information and recommendation system for e-commerce. In: IEEE International Conference on Industrial Engineering and Engineering Management, Jan 2016, pp. 611–615. https://doi.org/10.1109/IEEM.2015.7385720
Li, L.-H., Hsu, R.-W., Lee, F.-M.: Review of Recommender Systems and Their Applications. T&S Journal Publications (2012)
Kim, M.C., Chen, C.: A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics 104, 239–263 (2015)
Smith, B., Linden, G.: Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017)
Gigimol, S., John, S.: A survey on different types of recommendation systems. Eng. Sci. 1(4), 111–113 (2016)
Schafer, J., Konstan, A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1–2), 115–153 (2001). https://doi.org/10.1007/978-1-4615-1627-9_6
Elahi, M., Kholgh, D.K., Kiarostami, M.S., Saghari, S., Rad, S.P., Tkalčič, M.: Investigating the impact of recommender systems on user-based and item-based popularity bias. Inf. Process. Manage. 58(5), 102655 (2021)
Zhang, Y., Jiao, J.R.: An associative classification-based recommendation system for personalization in B2C e-commerce applications. Expert Syst. Appl. 33(2), 357–367 (2007)
Santhosh, N.M., Cheriyan, J., Sindhu, M.: An intelligent exploratory approach for product recommendation using collaborative filtering. In: 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)
Attokurov, U., Kaya, O., Sezgin, M.S.: Product recommendation based on embeddings: People who viewed this product also viewed these products. In: 2022 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 296–299. IEEE (2022)
Stöckli, D.R., Khobzi, H.: Recommendation systems and convergence of online reviews: The type of product network matters! Decis. Support Syst. 142, 113475 (2021)
Kaggle (2021). Available at: https://www.kaggle.com/. Last accessed 4 June 2023
Zhu, Z., Wang, S., Wang, F., Tu, Z.: Recommendation networks of homogeneous products on an E-commerce platform: measurement and competition effects. Expert Syst. Appl., 117128 (2022)
Chinchanachokchai, S., Thontirawong, P., Chinchanachokchai, P.: A tale of two recommender systems: the moderating role of consumer expertise on artificial intelligence based product recommendations. J. Retail. Consum. Serv. 61, 102528 (2021)
Lo, C., Yu, H., Yin, X., Shetty, K., He, C., Hu, K., Platz, J.M., Ilardi, A., Madhvanath, S.: Page-level optimization of e-commerce item recommendations. In: Proceedings of the 15th ACM Conference on Recommender Systems, pp. 495–504 (2021)
Zhao, X.: A study on e-commerce recommender system based on big data. In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 222–226. IEEE (2019)
Raghuwanshi, S.K., Pateriya, R.K.: Recommendation systems: techniques, challenges, application, and evaluation. In: Soft Computing for Problem Solving: SocProS 2017, vol. 2, pp. 151–164. Springer, Singapore (2019)
Prasad: A knowledge-based product recommendation system for e-commerce. Int. J. Intell. Inf. Database Syst. 1(1), 18–36 (2007). https://doi.org/10.1504/IJIIDS.2007.013283
De Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010)
Morgenthaler, S.: Exploratory data analysis. Wiley Interdiscip. Rev. Comput. Stat. 1(1), 33–44 (2009)
Hug, N.: Surprise: a Python library for recommender systems. J. Open Source Softw. 5(52), 2174 (2020)
Ravi Kumar, R.R.S., Appa Rao, G., Anuradha, S.: Efficient distributed matrix factorization alternating least squares (EDMFALS) for recommendation systems using spark. J. Inf. Knowl. Manage. 21(01), 2250012 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chitapulla, M.H., Assi, S., Bajnaid, W., Jayabalan, M., Al-Jumeily, D. (2024). Evaluation of Hybrid Recommendation System and Machine Learning Algorithms for E-Commerce Platform. In: Tan, A., et al. Advances in Intelligent Manufacturing and Robotics . ICIMR 2023. Lecture Notes in Networks and Systems, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-99-8498-5_31
Download citation
DOI: https://doi.org/10.1007/978-981-99-8498-5_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8497-8
Online ISBN: 978-981-99-8498-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)