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Intelligent Search & Personalization

Python Machine Learning Recommendation Search

Advanced Search and Recommendation Engine for Sony's Digital Media Ecosystem

Project Objective

To develop and implement a sophisticated search and recommendation system for Sony LIV's digital media platform, enhancing content discovery and user engagement through intelligent algorithms and personalized experiences.

  • Build semantic search capabilities for improved content discovery
  • Implement ML-driven personalization engine for content recommendations
  • Create scalable architecture to handle millions of user interactions

Project Description

The Intelligent Search & Personalization project revolutionized content discovery on Sony LIV by implementing hybrid search algorithms and machine learning-driven recommendation systems. The platform processes millions of user interactions daily to deliver personalized content experiences.

Semantic Search Engine

Advanced NLP-powered search that understands user intent, context, and content relationships beyond keyword matching.

Personalization Engine

ML algorithms that analyze viewing patterns, preferences, and behavior to deliver tailored content recommendations.

Real-time Analytics

Live user behavior tracking and analysis to continuously improve recommendation accuracy and search relevance.

Dynamic Content Filtering

Intelligent content categorization and filtering based on user preferences, demographics, and viewing history.

Technical Stack

Search & Analytics

Opensearch Kibana Logstash

Machine Learning & NLP

Recommendation Semantic Search Content based filtering Collaborative filtering

Backend & Infrastructure

Python AWS Redis

Leadership and Management

Team Leadership Project Management Cross-functional Collaboration Strategic Planning Agile Execution Mentorship & Talent Development

My Contribution

Search Architecture Design

Designed and implemented the search architecture fo semantic search using Elasticsearch, including index optimization, query performance tuning, and relevance scoring algorithms.

Machine Learning Pipeline Development

Built end-to-end ML pipelines for content recommendation using collaborative filtering, content-based filtering, and hybrid approaches

NLP Implementation

Developed natural language processing components for semantic search, including text preprocessing, embedding and query pipeline

Intelligent Autoscaling & Load Optimization

Implemented application-side autoscaling and load testing mechanisms driven by real-time trends data, ensuring system stability, optimal performance, and efficient resource utilization during high traffic periods.

Business Outcome

Improved Search Accuracy

Semantic search capabilities significantly improved content discovery and user satisfaction with search results.

Increase in User Engagement

Personalized recommendations led to longer viewing sessions and higher content consumption rates.

Cost Reduction

Achieved significant infrastructure cost savings by implementing application-side autoscaling and load testing based on real-time trends data.

Key Business Impacts:

  • Enhanced User Experience: Intelligent search and personalized recommendations created more engaging and satisfying user journeys
  • Increased Content Consumption: Better content discovery led to higher viewing rates and longer session durations
  • Scalable Infrastructure: Robust architecture handled millions of daily searches and recommendations without performance degradation
  • Data-Driven Insights: Real-time analytics provided valuable insights into user preferences and content performance