Personalized Product Recommendation System for Amazon

Abstract

Amazon\'s Personalised Product Recommendation System greatly enhances the shopping experience on the internet by providing product recommendations to users based on the type of items they have chosen to purchase on previous browsing histories and other actions that they have taken in the past. By using machine learning based techniques the system is able to accurately forecast products that will match the preferences of individual users which then leads to the improving customer satisfaction result together with an enhance conversion of sales figures from sales given over previous periods. Currently, in the current system, Amazon\'s recommendation engine mostly uses classical method, popularity-based filtering and collaborative filtering. These techniques, however, usually offer general advice which does not fully consider individual user preferences, resulting in lower engagement and unproductive product discovery. In the proposed system, a combination of leading recommendation algorithms (namely, Collaborative Filtering, Content-Based Filtering, Hybrid Models) is used to yield better quality and personalized suggestions. Various methods, including TF-IDF for text-based recommendation, Cosine Similarity for user-product relation, and Matrix Factorization (SVD) for latent feature extraction, are used to improve the quality of the recommendation. This paradigm guarantees an adaptive and personalised shopping experience that can be optimised both in terms of engagement of the user and revenue.

Introduction

Online shopping platforms like Amazon face challenges in helping users find products that match their preferences due to the vast product variety. Traditional recommendation methods—such as manual browsing, popularity-based suggestions, content-based filtering, and collaborative filtering—have limitations like lack of personalization, cold start problems (for new users), and scalability issues.

To address these challenges, the proposed system combines content-based filtering (analyzing product features) and collaborative filtering (leveraging user interactions) enhanced with advanced machine learning techniques. This hybrid approach analyzes user purchase history, ratings, and browsing behavior to provide accurate, personalized product recommendations, improving the overall shopping experience.

The system uses data from Amazon, including product metadata, user reviews, and interactions. It applies preprocessing steps to clean and standardize the data, and uses feature extraction methods like TF-IDF for product text and matrix factorization for user-item interactions. Combining the strengths of both filtering methods through weighted similarity scores enhances recommendation accuracy.

Various existing approaches (e.g., factorization machines, neural collaborative filtering, graph neural networks, reinforcement learning) are reviewed, noting their improvements but also drawbacks like high computational costs, scalability problems, cold start issues, and complexity.

The proposed hybrid recommendation model is evaluated using metrics like RMSE (Root Mean Squared Error), accuracy, and error rate, demonstrating superior performance compared to individual methods, thus providing more precise and personalized recommendations for users.

Conclusion

I we developed a Hybrid Recommendation System that combines the strengths of Content-Based Filtering (CBF) and Collaborative Filtering (CF) to improve recommendation accuracy. While Content-Based Filtering, which relies on item descriptions, achieved an accuracy of 78.2%, Collaborative Filtering, which analyzes user-item interactions, performed slightly better at 81.5%. However, by integrating both techniques, the Hybrid Model significantly improved accuracy to *87.3%, proving to be the most effective approach. Results show that a hybrid system gives personalized and broader recommendations which really hits the bullseye at what individual models lack. Cold starts on collaborative filtering can really be tricky and when we\'re dealing with sparse data content based filtering doesn\'t always get it quite right either. By combining them, we created a more balanced and efficient recommendation system. Now this is really great for real life applications like buying stuff on e stores and streaming videos. Recommendations really make a big difference - they make use of the shopping more and engage people more often to watch videos or buy things. Overall, our findings suggest that *hybrid recommendation systems are a powerful solution for delivering smarter, more relevant suggestions*, making them an ideal choice for modern recommendation engines.

References

[1] Jannach, Dietmar, et al. \"Hybrid recommendation systems for e-commerce: Combining collaborative filtering with content-based approaches.\" ACM Transactions on Intelligent Systems and Technology (2019). [2] Rendle, Steffen. \"Factorization machines with libFM.\" ACM Transactions on Intelligent Systems and Technology (2012). [3] He, Xiangnan, et al. \"Neural collaborative filtering.\" Proceedings of the 26th International Conference on World Wide Web. (2017). [4] Huang, Qiang, et al. \"Knowledge graph-based recommendation systems: A survey.\" ACM Computing Surveys (2020). [5] Koren, Yehuda, et al. \"Matrix factorization techniques for recommender systems.\" IEEE Transactions on Knowledge and Data Engineering (2009). [6] Sun, Zhiyong, et al. \"Attention-based recommendation models: A review and empirical analysis.\" IEEE Transactions on Neural Networks and Learning Systems (2021). [7] Wang, Xin, et al. \"Reinforcement learning-based recommendation: A survey and new perspectives.\" ACM Transactions on Information Systems (2020). [8] Zhang, Hongwei, et al. \"Graph neural networks for recommendation: A survey.\" IEEE Transactions on Big Data (2022). [9] Li, X., et al. \"Session-based recommendation with RNNs: A comprehensive study.\" ACM Transactions on Information Systems (2021). [10] Chen, Y., et al. \"A hybrid recommendation model combining collaborative filtering, deep learning, and knowledge graphs.\" IEEE Transactions on Artificial Intelligence (2022).

Copyright

Copyright © 2025 Narendran M, Mr. Syedsafi S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.