Job Recommendation System - Semantic Search
MY ROLES
TIMELINE
2021
TOOLS USED
Recommendation Systems, LLMs, Embeddings, Python, Semantic Search, Similarity Ranking, Natural Language Processing, Data Engineering, Content-Based Filtering, Career Development
DESCRIPTION
This project, developed as part of DataRebel.io, creates personalized job recommendations by analyzing users' posts, projects, data interests, and engagement patterns. The system leverages embeddings and semantic similarity to identify job postings that align with users' skills and preferences, ranking opportunities based on their relevance probability. This enhances the DataRebel platform by connecting users with career options that match their demonstrated expertise and interests.
OUTCOMES
- Developed an automated job recommendation system that increased user engagement with job listings by 35%
- Created embedding-based matching algorithm that effectively captured semantic similarities between user interests and job requirements
- Integrated the system with the DataRebel.io platform to provide value-added services for users sharing their projects and data
FEATURES
User activity tracking, content embedding generation, similarity scoring algorithms, personalized ranking, integrated job recommendation display, profile-based customization
CHALLENGES
Creating meaningful embeddings from diverse user content types (posts, projects, likes). Balancing personalization with discovery of new opportunities. Handling cold-start problems for new users with limited platform activity.
APPROACH
Generated embeddings from user-generated content and activity on the platform. Implemented a similarity-based ranking system that matched these embeddings against job posting descriptions and requirements. Sorted opportunities by similarity score to present the most relevant matches first. Integrated the system within the DataRebel.io ecosystem to enhance the platform's value proposition for data professionals.