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 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).

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.

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.

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.

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.

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 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.