Create recommendation explainability system with reasoning

[anshul23102]

Description

No explanations reduce trust. Transparency improves trust 70%, enables refinement

Steps to Reproduce

• Feature currently unavailable in system
• Affects user experience and platform capability

Environment Information

• Platform: Existing stack
• Integration: Fits with current architecture

Expected Behavior

• Feature fully implemented
• Performance and quality standards met
• Integration with existing systems

Actual Behavior

• Feature missing or incomplete
• Users unable to access capability

Additional Context

Root Cause: Not implemented.

Proposed Solution: See implementation below.

Implementation Steps:

1. Design system architecture
2. Implement core functionality
3. Build UI/UX as needed
4. Comprehensive testing
5. Performance optimization
6. Documentation and guides
7. User feedback collection
8. Model training/tuning (if ML)
9. Analytics and monitoring
10. Deployment and launch

Test Cases (5 major, 5-6 test cases):

• Core functionality test
• Edge case handling
• Performance validation
• Integration testing
• User experience validation
• Security testing
• Scale testing
• Error handling
• Recovery scenarios
• Analytics verification

Severity: High - important feature
Expected Points: 500-600 GSSoC points

Suggested Labels

enhancement, features, GSSoC26

[0000001abhishek-debug]

/assign

under GSSoC

[anshul23102]

@siddu-k Could you please assign this issue to me? I'd like to work on it under GSSoC '26.

I opened this issue and have a detailed implementation plan:

• Build a reasoning layer that explains why each command suggestion was made, using the user's shell history and context as input
• Add an explainability endpoint that returns natural-language rationale alongside each recommendation
• Integrate a lightweight explanation widget in the UI that can be expanded/collapsed
• Track explanation acceptance rate as a metric to measure trust improvement
• Write tests covering explanation generation and UI rendering

Ready to start immediately on assignment.

[anshul23102]

Hi @siddu-k, I'd like to work on this. Plan: add a reasoning layer to the recommendation engine that surfaces the key factors (similar users, item features, past interactions) behind each recommendation, and expose it via an explanation field in the API response. Requesting assignment under GSSoC 2026.

/assign
/claim