ProtonMart - AI Driven E-Commerce Platform For Electronic Goods Using Collaberative Filtering Algorithm
DOI:
https://doi.org/10.26438/ijcse/v12i7.18Keywords:
E-commerce, Django, SQLite3, Ajax, PayPal, Collaborative Filtering, ElectronicsAbstract
This research paper investigates the development of a Django-based e-commerce platform specializing in the sale of electronic goods, augmented with a user-based collaborative filtering algorithm for personalized product recommendations. In the competitive landscape of online retail, providing tailored recommendations to users is crucial for improving user engagement and driving sales. Leveraging Django framework, SQLite3 database, AJAX technology, and PayPal integration , this study explores the integration of collaborative filtering into the e-commerce framework to enhance user experience and boost sales. key features of this platform includes a search bar, brand and category filters, an administrative interface, shopping cart functionality, and integration with PayPal payment gateway. Subsequently, the research details the incorporation of a user-based collaborative filtering algorithm for product recommendations.
References
[1] S. Gupta and A. Singh, "Personalized Product Recommendations in E-commerce Using Collaborative Filtering and Deep Learning," 2023 IEEE International Conference on Data Science and Machine Learning (ICDSML), New York, NY, USA, pp.112-116, 2023.
[2] J. Patel and R. Shah, "Dynamic User Preferences Modeling for Personalized Recommendations in E-commerce Using Temporal Collaborative Filtering," 2023 IEEE International Conference on Big Data and Analytics (ICBDA), Sydney, Australia, pp.45-49, 2023.
[3] M. Sharma and P. Mishra, "Privacy-Preserving Collaborative Filtering for Personalized Product Recommendations in Ecommerce," 2023 IEEE International Conference on Privacy, Security and Trust (PST), Toronto, Canada, pp.220-224, 2023.
[4] K. Mehta and S. Jain, "Real-time Personalized Product Recommendations in E-commerce Using Apache Spark and Collaborative Filtering," 2023 IEEE International Conference on Cloud Computing and Big Data (CCBD), Barcelona, Spain, pp.88-92, 2023.
[5] Ricci, F., Rokach, L. and Shapira, B., 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, Boston, MA.M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, pp.1-35, 1989.
[6] Jannach, D., Zanker, M., Felfernig, A. and Friedrich, G., Recommender systems: an introduction. Cambridge University Press, 2010.
[7] Shani, G., Gunawardana, A., Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA, 2011.
[8] Thorat, P.B., Goudar, R.M. and Barve, S., Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, Vol.110, Issue.4, pp.31-36, 2015.
[9] N. Gupta and R. Sharma, "Context-Aware Personalized Recommendations in E-commerce Using Collaborative Filtering," 2023 IEEE International Conference on Internet of Things (IoT), Paris, France, pp.375-380, 2023.
[10] A. Verma and S. Kumar, "Adversarial Attacks and Defenses in Personalized Product Recommendation Systems," 2023 IEEE International Conference on Cybersecurity and Privacy (ICSP), Seoul, South Korea, pp.135-139, 2023.
[11] R. Sharma, S. Rani and S. Tanwar, "Machine Learning Algorithms for building Recommender Systems," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, pp.785790, 2019.
[12] Yash Patil, Samidha Ashtikar, Sakshi Shirodkar, Krishna Dudhate, Shraddha V. Pandit, "Recommendation Systems in Online Retail: A Comprehensive Survey of AI Techniques", International Journal of Computer Sciences and Engineering, Vol.12, Issue.2, pp.30-36, 2024.
[13] E. Gupta and R. Singh, "Multi-Modal Collaborative Filtering for Personalized Recommendations in E-commerce Using Graph Neural Networks," 2023 IEEE International Conference on Multimedia and Expo (ICME), Amsterdam, Netherlands, pp.25-29, 2023.
[14] B. Kumar and S. Sharma, "Federated Learning for Personalized Recommendations in Decentralized E-commerce Environments," 2023 IEEE International Conference on Parallel and Distributed Systems (ICPADS), Taipei, Taiwan, pp.190-194, 2023.
[15] R. Gupta and A. Kumar, "Hybrid Collaborative Filtering and ContentBased Filtering for Personalized Recommendations in E-commerce," 8 2023 IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD), Rome, Italy, pp.55-59, 2023.
[16] D. Sharma and S. Patel, "Sequential Collaborative Filtering for Personalized Recommendations in E-commerce Using Recurrent Neural Networks," 2023 IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, , pp.300-305, 2023.
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