# Deep Learning-Based Intelligent News Recommendation System: Complete Implementation from TF-IDF to Multi-Architecture Neural Networks

> This article introduces a complete open-source news recommendation project covering data preprocessing, TF-IDF feature extraction, SMOTE class balancing, and comparative experiments of FNN, LSTM, RNN, and CNN-LSTM hybrid architectures. Based on the author's self-built Kaggle dataset, the project provides a reproducible technical solution for personalized news analysis.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-03T17:44:22.000Z
- 最近活动: 2026-06-03T17:48:06.203Z
- 热度: 156.9
- 关键词: news recommendation, deep learning, LSTM, RNN, TF-IDF, SMOTE, NLP, text classification, neural networks, Python, TensorFlow
- 页面链接: https://www.zingnex.cn/en/forum/thread/tf-idf-c199be0f
- Canonical: https://www.zingnex.cn/forum/thread/tf-idf-c199be0f
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## Project Guide to Deep Learning-Based Intelligent News Recommendation System

This article introduces a complete open-source news recommendation project, covering data preprocessing, TF-IDF feature extraction, SMOTE class balancing, and comparative experiments of multi-architecture neural networks such as FNN, LSTM, RNN, and CNN-LSTM. The project is based on the author's self-built Kaggle dataset and provides a reproducible technical solution for personalized news analysis. The tech stack includes Python, TensorFlow, Keras, etc. The original author is Ankur Ray Chayan, the project is open-sourced on GitHub, and the dataset is published on Kaggle (DOI:10.34740/kaggle/ds/6291355).

## Challenges of News Recommendation in the Age of Information Overload

The explosive growth of internet news platforms leads to information overload, making it difficult for users to efficiently find content of interest. Traditional methods rely on manual editing or simple keyword matching, which cannot capture deep semantics and users' personalized preferences. The core challenge of news recommendation systems lies in enabling machines to understand the meaning of content and accurately push it based on users' historical behavior, which requires strong NLP capabilities and models that can handle high-dimensional sparse text data.

## Details of Data Preprocessing and Feature Engineering

### Text Cleaning Process
Missing value handling → Stopword removal → Special character cleaning → Lemmatization → Label encoding
### TF-IDF Feature Extraction
Covers news title, description, and source information, converting text into numerical vectors to capture topic content and source features
### SMOTE Class Balancing
Uses SMOTE technology to synthesize minority class samples, solving the class imbalance problem in news data and preventing the model from being biased towards the majority class.

## Detailed Explanation of Multi-Architecture Neural Networks

1. **FNN Baseline Model**: Fully connected neural network, computationally efficient, used as a reference for complex models
2. **LSTM**: Solves the long-sequence gradient vanishing problem, captures long-distance semantic dependencies
3. **Standard RNN**: Models text temporal features, suitable for short text local context
4. **RNN+Dense Hybrid Architecture**: Combines RNN sequence modeling with Dense layer nonlinear transformation
5. **CNN-LSTM Hybrid Architecture**: CNN extracts local n-gram features, LSTM models sequence relationships, often achieves optimal performance.

## Model Evaluation and Performance Comparison

Evaluated using metrics such as accuracy, precision, recall, F1-Score, confusion matrix, ROC curve, and AUC. Comparative results: LSTM and CNN-LSTM perform better in long text semantic processing, while FNN serves as a baseline balancing computational efficiency and performance.

## Practical Application Scenarios and Tech Stack

### Application Scenarios
- Personalized news recommendation: Improve user stickiness
- Automatic content classification: Enhance editorial efficiency
- Information retrieval optimization: Precise search results
- Intelligent content analysis: Analyze news trends and public opinion
### Tech Stack
TensorFlow/Keras, NLTK, NeatText, Scikit-Learn, Imbalanced-Learn, Pandas/NumPy, Matplotlib/Seaborn.

## Future Directions and Project Summary

### Future Development Directions
- Integration of Transformer architectures (BERT/RoBERTa)
- Construction of real-time recommendation systems
- Enhancement of explainable recommendations
- Fusion of large language models
### Summary Insights
1. Data quality is the foundation
2. Multi-architecture comparison is necessary
3. Engineering completeness determines usability
4. Class balancing cannot be ignored
The project is open-sourced under the MIT license, and the code and dataset are available on GitHub and Kaggle, providing references for academia and industry.