AI Driven Compatibility Scoring and Ranking System Workflow

Compatibility Scoring and Ranking System

1. Data Collection

1.1 User Profile Data

Gather user information through surveys and questionnaires that cover demographics, interests, values, and preferences.

1.2 Behavioral Data

Utilize AI-driven tools, such as Google Analytics and Mixpanel, to track user interactions within the app, including swipes, messages, and match rates.

2. Data Processing

2.1 Data Cleaning

Implement data cleaning techniques using Python libraries like Pandas to ensure the accuracy and quality of collected data.

2.2 Data Normalization

Standardize user data to ensure compatibility scores are calculated on a uniform scale, using tools like R or SQL for normalization processes.

3. Compatibility Scoring Algorithm

3.1 Feature Selection

Identify key features influencing compatibility, such as shared interests, lifestyle choices, and personality traits.

3.2 AI Model Development

Develop a machine learning model using TensorFlow or Scikit-learn to predict compatibility scores based on selected features.

3.3 Scoring Mechanism

Calculate compatibility scores using the model, assigning weights to different features based on their importance, and generate scores on a scale of 1 to 100.

4. Ranking System

4.1 Score Aggregation

Aggregate compatibility scores for all potential matches and rank them in descending order to identify the best matches for the user.

4.2 Dynamic Updates

Implement a feedback loop where user interactions with matches refine the scoring algorithm over time, utilizing reinforcement learning techniques.

5. User Interface Integration

5.1 Dashboard Development

Create a user-friendly dashboard that displays compatibility scores and ranked matches, utilizing UI design tools like Figma or Adobe XD.

5.2 Personalized Recommendations

Incorporate AI-driven recommendation systems, such as collaborative filtering, to suggest new matches based on user behavior and preferences.

6. Continuous Improvement

6.1 User Feedback Collection

Solicit user feedback on match quality and overall experience through in-app surveys and ratings.

6.2 Model Refinement

Regularly update the AI model with new data and user feedback to enhance accuracy and relevance of compatibility scoring.

7. Reporting and Analytics

7.1 Performance Metrics

Track key performance indicators (KPIs) such as match success rate and user satisfaction scores using BI tools like Tableau or Power BI.

7.2 Data Visualization

Utilize data visualization tools to present findings and trends, enabling informed decision-making for future developments in the dating tool.