Sentiment Based Product Recommendation System for E Commerce Using Machine Learning Approaches

Muzakkiruddin Ahmed Mohammed

doi:10.55524/

Authors

Muzakkiruddin Ahmed Mohammed B. Tech Scholar, Department of Electrical and Electronics Engineering, Lords Institute of Engineering and Technology, Hyderabad, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Recommender Systems, Logistic Regression and Analysis, Random Forest, Xgboost, Hyperparameter Tuning, Deployment

Abstract

Today, e-commerce is a thriving industry. We do not need to approach every customer to accept their orders here. A business creates a website to offer things to clients, who can then purchase the stuff they need within the same website. These e-commerce firms include well known ones like Amazon, Shopify, Myntra, Flipkart, and Ajio. To create a product recommendation system for the end customers, we will be using the data set of e-commerce product reviews in this final project. A sentiment analysis model will be used to enhance the suggestions. Under this final project, we will develop a sentiment analysis engine utilising a variety of machine learning approaches before selecting the model that produces the best results.

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References

E. Turban, D. King, J. Lee and D. Viehland, Electronic Commerce: A Managerial Perspective, Upper Saddle River, NJ, USA: Prentice-Hall, 2002.

Liu, J.G., Zhou, T., Wang, B.H.: Research Progress of Personalized Recommendation System. Progress in Natural Science 19(1), 1–15 (2009)

J.B. Schafer, J. Konstan and J. Riedl, "Recommender systems in e-commerce", Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166, 1999, November.

M.B. Dias, D. Locher, M. Li, W. El-Deredy and P.J. Lisboa, "The value of personalised recommender systems to e business: a case study", Proceedings of the 2008 ACM conference on Recommender systems, pp. 291-294, 2008, October.

Recommendation Systems: Applications Examples & Benefits, 2020, [online] Available: https://research.aimultiple.com/recommendation

system/#media.

M. Sree Vani, "IJARCCE A Recommender System for Online Advertising", International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 2, 2016.

Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim and R. Kashef, "Recommendation Systems: Algorithms Challenges Metrics and Business Opportunities", Applied Sciences, vol. 10, no. 21, pp. 7748, 2020.

Linden, Greg, Brent Smith, and Jeremy York. "Amazon.com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7.1 (2003): 76-80

Park, Sung Eun, Sangkeun Lee, and Sang-goo Lee. "Session based collaborative filtering for predicting the next song." Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on. IEEE, 2011.

Suksawatchon Ureerat, Sumet Darapisut, and Jakkarin Suksawatchon. "Incremental session based collaborative filtering with forgetting mechanisms." 2015 International Computer Science and Engineering Conference (ICSEC). IEEE, 2015.

Mustafa, Ghulam, and Ingo Frommholz. "Performance comparison of top N recommendation algorithms." 2015 Fourth International Conference on Future Generation Communication Technology (FGCT). IEEE, 2015.

Oard, Douglas W., and Jinmook Kim. "Implicit feedback for recommender systems." Proceedings of the AAAI workshop on recommender systems. 1998.

Romadhony, Ade, Said Al Faraby, and Bambang Pudjoatmodjo. "Online shopping recommender system using hybrid method." Information and Communication Technology (ICoICT), 2013 International Conference of. IEEE, 2013.