Crafting a Job Matching Algorithm

Requirements Clarification & Assessment

1. Understanding User and Job Profiles:

   • LinkedIn Profiles: Analyze user profiles to extract demographic data, skills, past job titles, industries, and educational background.
   • Job Application History: Record of jobs users have applied to, which helps in understanding user preferences and intent.
   • Questionnaire Responses: Insights into user preferences, desired job characteristics, and other subjective criteria.

2. System Objectives:

   • Recommendation Accuracy: Prioritize matching jobs that align with user qualifications and preferences.
   • User Engagement: Ensure recommendations encourage user interaction, such as clicks and applications.
   • Business Goals: Balance between recommending high-profile companies and lesser-known ones to ensure equitable exposure.

3. Technical Constraints:

   • Data Volume: Efficiently handle large datasets of user profiles and job listings.
   • Response Time: Ensure the system provides recommendations swiftly to maintain a seamless user experience.
   • Cold Start Problem: Address challenges related to new users or jobs with limited historical data.

4. Performance Metrics:

   • Precision & Recall: Evaluate the accuracy of recommendations.
   • Click-Through Rate (CTR): Measure user engagement with recommended jobs.
   • Application Rate: Track the conversion from recommendations to job applications.

5. User Experience:

   • Interface Design: Recommendations should be easily accessible and intuitively presented.
   • Feedback Mechanism: Allow users to provide feedback on recommendations to improve future suggestions.