Smart Recommendations: Contributor Discovery & Opportunity Matching

[bdougie]

Overview

This issue covers the Smart Recommendations features originally planned for Phase 2 (#803) but deferred to maintain focus on core AI enrichment capabilities. These features build on the AI-powered contributor analysis to provide intelligent matching and discovery.

Background

With Phase 2's backend complete, we now have:

• Topic clustering (content + contributor expertise)
• Persona detection (7 persona types)
• Quality scoring (multi-factor assessment)
• Trend analysis (velocity + topic shifts)

These capabilities enable sophisticated recommendation algorithms for contributor discovery and opportunity matching.

Features to Implement

Similar Contributor Discovery

Goal: Find contributors with similar interests/expertise

Implementation:

• Use contributor embeddings from topic clustering
• Calculate cosine similarity between contributors
• Surface "contributors like this one" recommendations
• Group by expertise clusters for exploration

UI Placement: Contributor profile sidebar

Contribution Opportunity Matching

Goal: Match contributors to relevant issues based on their history

Implementation:

• Compare issue embeddings with contributor expertise embeddings
• Score matches based on:
  • Topic similarity (60%)
  • Historical success in similar areas (25%)
  • Current activity level (15%)
• Filter by difficulty level matching contributor experience

UI Placement:

• "Recommended for you" section in issue lists
• "Good first issues" personalized for each contributor

Reviewer Suggestions

Goal: Suggest reviewers for PRs based on expertise

Implementation:

• Analyze PR content embedding
• Match against contributors with:
  • Relevant topic expertise
  • High code review depth scores
  • Recent activity in repository
• Rank by quality score + topic match

UI Placement: PR reviewer assignment dropdown

Mentorship Opportunities

Goal: Connect question-askers with domain experts

Implementation:

• Identify contributors with high mentor scores
• Match discussion topics with expert topics
• Surface experts when contributors ask questions
• Suggest mentorship pairings

UI Placement: Discussion threads, contributor profiles

Technical Implementation

Similarity Algorithms

Contributor Similarity:

function findSimilarContributors(
  contributorId: string,
  limit: number = 5
): Promise<SimilarContributor[]> {
  // 1. Get contributor embedding from topic_clustering
  // 2. Calculate cosine similarity with all other contributors
  // 3. Filter by similarity threshold (> 0.7)
  // 4. Sort by similarity score
  // 5. Return top N with explanation
}

Issue Matching:

function matchContributorToIssues(
  contributorId: string,
  repositoryId: string
): Promise<IssueMatch[]> {
  // 1. Get contributor expertise embedding
  // 2. Get all open issues with embeddings
  // 3. Score each issue by similarity + additional factors
  // 4. Return ranked matches with confidence
}

Data Requirements

Existing Data (from Phase 2):

• ✅ Contributor embeddings (aggregated expertise)
• ✅ Issue/PR/Discussion embeddings (content)
• ✅ Topic clusters
• ✅ Quality scores
• ✅ Persona classifications

New Data Needed:

• Historical success tracking (issues resolved in specific topics)
• Reviewer effectiveness metrics
• Mentorship interaction history

Privacy & Ethics

• Transparent recommendations - Show why someone was recommended
• Opt-out capability - Contributors can disable recommendation features
• Diversity considerations - Avoid creating filter bubbles
• Bias monitoring - Track recommendation diversity across demographics
• Public data only - No external enrichment

Success Metrics

• Discovery accuracy: 80% of "similar contributors" are validated as similar by maintainers
• Match quality: 60% of recommended issues result in contributions
• Reviewer effectiveness: Suggested reviewers 30% more likely to provide quality reviews
• Time savings: Reduce time finding right contributor by 50%

Implementation Phases

Phase 1: Similar Contributor Discovery (1 week)

• Implement cosine similarity algorithm
• Build recommendation API endpoint
• Create UI component for "Similar Contributors"
• Add to contributor profile page

Phase 2: Issue Matching (2 weeks)

• Build issue scoring algorithm
• Create "Recommended for You" feature
• Integrate with issue lists
• Add filtering and ranking controls

Phase 3: Reviewer Suggestions (1 week)

• Implement reviewer scoring
• Integrate with PR creation flow
• Add auto-suggest to reviewer dropdown
• Track suggestion effectiveness

Phase 4: Mentorship Matching (1 week)

• Build expert identification system
• Create mentorship opportunity detection
• Add UI prompts for mentorship
• Track mentorship outcomes

Technical Considerations

Performance:

• Pre-compute similarity matrices for frequent lookups
• Use materialized views for ranking queries
• Cache recommendations with TTL

Scalability:

• Batch similarity calculations
• Incremental updates to recommendations
• Pagination for large result sets

Quality Assurance:

• A/B test recommendation algorithms
• Collect feedback on recommendation quality
• Adjust weights based on effectiveness data

Related Issues

• Parent issue: Contributor CRM System: AI Enrichment #803 (AI Enrichment - Phase 2)
• Depends on: PR feat: Phase 2 AI-powered contributor enrichment (#803) #1143 (Phase 2 backend implementation)
• Related: Contributor Management System - CRM-focused approach (follow-up to #598) #685 (Original Contributor Management System proposal)

Priority

Priority: MEDIUM (deferred from Phase 2)
Estimated Effort: 5-6 weeks
Dependencies: Phase 2 backend complete ✅

Notes

This work was originally part of #803 but separated to:

1. Keep Phase 2 PR focused and reviewable
2. Allow Phase 2 UI work to proceed independently
3. Enable separate prioritization of recommendation features
4. Provide clearer scope for future implementation