# Smart E-commerce Recommendations with Semantic AI

arXiv: https://arxiv.org/abs/2409.01137 · HTML: https://mlsanity.com/paper/2409.01137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [What's Hidden in a Randomly Weighted Neural Network?](https://mlsanity.com/paper/1911.13299/) (2019)
- [Neural Network Approximation: Three Hidden Layers Are Enough](https://mlsanity.com/paper/2010.14075/) (2020)
- [Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks](https://mlsanity.com/paper/2105.12374/) (2021)
- [Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics](https://mlsanity.com/paper/2011.04412/) (2020)
- [Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce](https://mlsanity.com/paper/2010.12540/) (2020)

## Abstract (arXiv)

In e-commerce, web mining for page recommendations is widely used but often fails to meet user needs. To address this, we propose a novel solution combining semantic web mining with BP neural networks. We process user search logs to extract five key features: content priority, time spent, user feedback, recommendation semantics, and input deviation. These features are then fed into a BP neural network to classify and prioritize web pages. The prioritized pages are recommended to users. Using book sales pages for testing, our results demonstrate that this solution can quickly and accurately identify the pages users need. Our approach ensures that recommendations are more relevant and tailored to individual preferences, enhancing the online shopping experience. By leveraging advanced semantic analysis and neural network techniques, we bridge the gap between user expectations and actual recommendations. This innovative method not only improves accuracy but also speeds up the recommendation process, making it a valuable tool for e-commerce platforms aiming to boost user satisfaction and engagement. Additionally, our system ability to handle large datasets and provide real-time recommendations makes it a scalable and efficient solution for modern e-commerce challenges.