Z-ENG AI Based Internal Linking Recommendation System 2
2024-2025 tavasz
Szoftver
Téma leírása
Internal linking is a key factor in SEO, helping search engines understand website structure and improving rankings. Effective internal linking distributes authority across pages, enhances crawlability, and improves user navigation.
Manually identifying internal link opportunities is time-consuming, especially for large websites.
This project aims to automate the process using machine learning and graph-based models:
1. Analyze website structure and content to identify missing internal links.
2. Develop a machine learning model that predicts high-value internal links based on topic relevance and existing link structure.
3. Implement a lightweight recommendation system for real-time link suggestions.
4. Use Explainable AI (XAI) to provide transparency in link recommendations.
Tasks
1. Data Collection
Crawl website pages to extract URLs, anchor texts, and existing internal links. Identify pages with poor internal linking.
2. Feature Engineering
Extract keywords and topics from each page.
Build a graph-based model where pages are nodes and links are edges.
Identify content clusters to determine link opportunities.
3. Machine Learning for Link Prediction
Train a graph-based model (Node2Vec, Graph Neural Networks) to predict missing links.
Use topic similarity, existing links, and authority flow as input features.
4. Model Distillation
Convert deep learning models into lightweight rule-based systems for real-time use.
5. Explainability
Use SHAP or LIME to explain why certain links are recommended.
Highlight key factors influencing recommendations, such as keyword similarity and content relevance.
Expected Outcome
A machine learning system that suggests internal links to improve SEO.
A structured website graph that identifies content relationships.
A lightweight and explainable model that provides clear link recommendations.
Tools: Scrapy, Scikit-learn, Neo4j, SHAP/LIME
Feltételek
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