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Personalized News Recommendation System

Built personalized recommendation system using entity recognition & knowledge graphs, significantly boosting user engagement and retention on news platform.

2 min read

Impact

Increased user engagement, improved retention, real-time updates

Recommendation Systems
NLP
Knowledge Graphs
Real-time Processing

Overview

Built a sophisticated personalized recommendation system for a news platform using entity recognition and knowledge graphs for content relevance improvement. The system integrates trending news summaries with a real-time recommendation engine to boost user engagement and interaction.

Technical Architecture

NLP & Entity Recognition

  • Named Entity Recognition (NER) for content tagging
  • Topic modeling and categorization
  • Sentiment analysis for content mood
  • Entity linking to knowledge bases

Knowledge Graph

  • Graph database for entity relationships
  • Content similarity networks
  • User interest graphs
  • Trending topic tracking

Recommendation Engine

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation approach
  • Real-time update mechanism

Key Features

  • Personalized Content: Tailored news recommendations based on user interests
  • Entity-Based Matching: Uses entity recognition to understand content relationships
  • Trending Integration: Incorporates trending news summaries
  • Real-Time Updates: Dynamic recommendations as news breaks
  • Enhanced Engagement: Optimized for user retention and interaction

Technical Challenges & Solutions

Challenge: Cold Start Problem

New users and new articles lacked historical data for recommendations.

Solution: Implemented hybrid approach combining content-based filtering (using entities and topics) with popularity-based recommendations for cold start scenarios. Knowledge graphs helped establish initial content relationships.

Challenge: Real-Time Processing at Scale

News platform required instant recommendations as new articles published.

Solution: Built streaming pipeline for real-time entity extraction and graph updates. Implemented caching strategies and pre-computed similarity scores for frequently accessed content.

Challenge: Diverse User Interests

Users had varied and evolving interests requiring adaptive recommendations.

Solution: Designed dynamic user interest graphs that updated based on reading patterns, implemented time-decay for evolving preferences, and used entity-level tracking (rather than just article-level) for fine-grained understanding.

Impact

  • Increased User Engagement: Significantly boosted interaction across news platform
  • Improved Retention: Enhanced user retention through relevant content delivery
  • Real-Time Relevance: Dynamic recommendations kept content fresh and timely
  • Better User Experience: Personalization led to higher satisfaction metrics

Technologies Used

  • NLP: spaCy, Named Entity Recognition, Topic Modeling
  • Graph DB: Neo4j / NetworkX for knowledge graphs
  • Recommendation: Collaborative & content-based filtering
  • Real-Time: Streaming processing for live updates
  • Backend: Python, FastAPI

Innovation

  • Knowledge Graph Integration: Novel use of entity relationships for news recommendation
  • Hybrid Approach: Combined multiple recommendation strategies for robustness
  • Real-Time Adaptation: Dynamic system responding to breaking news and trends