InspireAI - Integrated Recommendation System

As part of my work with InspireAI, I integrated a comprehensive end-to-end recommendation system designed to deliver personalized content based on users’ web-scraped LinkedIn profiles. The system was built to help users discover articles and insights aligned with their professional interests and career goals.

Key Contributions:
LinkedIn Profile Scraping: Developed a system to scrape and extract relevant data from LinkedIn profiles, ensuring rich user information for recommendations.
NLP Pre-processing & Word2Vec: Created a robust NLP pipeline to process user profile data, followed by Word2Vec vectorization for converting textual data into meaningful word embeddings.
Clustering & Recommendations: Applied K-Means clustering to categorize similar posts and articles, enabling the system to recommend personalized, contextually relevant content to users.

Key Features:
Personalized Content: Recommends articles, posts, and insights based on users’ professional background and interests.
Scalable Model: Built for scalability, capable of handling large datasets and evolving user preferences.

This recommendation engine effectively uses advanced NLP techniques and machine learning to enhance the user experience, providing tailored recommendations for career growth and industry insights.