ProfileMatching/CHANGELOG.md

# ProfileMatching Changelog

## 2025-11-04 - Initial Module Creation & URL Resolution Integration

### Module Organization
- Created dedicated `ProfileMatching/` folder within UnifiedContacts
- Bundled all matching and verification scripts together
- Added comprehensive interactive `main.py` with menu system
- Added detailed `README.md` documentation

### Major Enhancement: URL Resolution Integration

**Problem**: Missing matches where Twitter usernames differ from Telegram usernames, even when Twitter profile explicitly links to Telegram.

**Solution**: Integrated `url_resolution_queue` table data into candidate finding.

**Implementation**:
- Added new method `find_by_resolved_url()` to TwitterMatcher class (find_twitter_candidates.py:339-378)
- Queries Twitter profiles where bio URLs (t.co/xyz) resolve to t.me/{username}
- Integrated as Method 5b in candidate finding pipeline (find_twitter_candidates.py:554-574)
- Baseline confidence: 0.95 (very high - explicit link in bio)

**Impact**:
- 140+ potential new matches identified
- Captures matches like Twitter @Block_Flash → Telegram @bull_flash
- Especially valuable when usernames differ but user explicitly links profiles

**Example Match**:
```
Twitter: @Block_Flash (ID: 63429948)
Telegram: @bull_flash
Method: twitter_bio_url_resolves_to_telegram
Confidence: 0.95
Resolved URL: https://t.me/bull_flash
Original URL: https://t.co/dc3iztSG9B
```

### URL Resolution Data Source

The `url_resolution_queue` table contains:
- 16,133 Twitter profiles with resolved Telegram URLs
- Shortened URLs from Twitter bios (t.co/xyz)
- Resolved destinations (full URLs)
- Extracted Telegram handles (JSONB array)

### Files Modified

1. **find_twitter_candidates.py**
   - Added `find_by_resolved_url()` method
   - Integrated into `find_candidates_for_contact()` as Method 5b
   - Fixed type casting for twitter_id (VARCHAR) to user.id (BIGINT) join

2. **main.py** (ProfileMatching module)
   - Created comprehensive interactive menu
   - Real-time statistics display
   - Streamlined workflow for candidate finding and LLM verification

3. **README.md**
   - Complete documentation of all 10 matching methods
   - Usage examples and performance metrics
   - Configuration and troubleshooting guides

4. **UnifiedContacts/main.py**
   - Added option 15: "Open Profile Matching System (Interactive Menu)"
   - Reorganized menu to separate ProfileMatching from individual Twitter steps

### Current Statistics (as of 2025-11-04)

**Candidates**:
- Users with candidates: 38,121
- Total candidates found: 253,117
- Processed by LLM: 253,085
- Pending verification: 32

**Verified Matches**:
- Users with matches: 25,662
- Total matches: 36,147
- Average confidence: 0.74
- High confidence (90%+): 12,031
- Medium confidence (80-89%): 1,452
- Low confidence (70-79%): 7,505

### LLM Verification (V6 Prompt)

Current prompt improvements:
- Upfront directive for comparative evaluation
- Clear signal strength hierarchy (Very Strong, Strong Supporting, Weak, Red Flags)
- Company vs personal account differentiation
- Streamlined from ~135 to ~90 lines while being clearer
- Emphasis on evaluating ALL candidates together

### Performance Metrics

**Candidate Finding (Threaded)**:
- Speed: ~1.5 contacts/sec
- Time for 43K contacts: ~16-18 hours
- Workers: 8 (default)

**LLM Verification (Async)**:
- Speed: ~32 users/minute (100 concurrent requests)
- Cost: ~$0.003 per user (GPT-5-mini)
- Time for 43K users: ~23 hours

### Module Structure

```
ProfileMatching/
├── main.py                              # Interactive menu system
├── README.md                            # Complete documentation
├── CHANGELOG.md                         # This file
├── find_twitter_candidates.py           # Core matching logic (10 methods)
├── find_twitter_candidates_threaded.py  # Threaded implementation
├── verify_twitter_matches_v2.py         # LLM verification (V6 prompt)
├── review_match_quality.py              # Analysis tools
└── setup_twitter_matching_schema.sql    # Database schema
```

### 10 Matching Methods Summary

1. **Phash Match** (0.95/0.88) - Profile picture similarity
2. **Exact Bio Handle** (0.95) - Twitter handle extracted from Telegram bio
3. **Bio URL Resolution** (0.95) ⭐ NEW - Shortened URL resolves to Telegram
4. **Twitter Bio Has Telegram** (0.92) - Twitter bio mentions Telegram username
5. **Display Name Containment** (0.92) - TG name in TW name
6. **Exact Username** (0.90) - Usernames match exactly
7. **TG Username in Twitter Name** (0.88)
8. **Twitter Username in TG Name** (0.86)
9. **Fuzzy Name** (0.65-0.85) - Trigram similarity
10. **Username Variation** (0.80) - Generated username variations

### Testing

All changes tested with:
- Standalone method testing (find_by_resolved_url)
- Full integration testing (find_candidates_for_contact)
- Verified deduplication works correctly
- Confirmed matches with different usernames are captured

### Next Steps

Potential future enhancements:
- Add more matching methods (location, bio keywords, mutual connections)
- Implement feedback loop for prompt improvement
- Add manual review interface for borderline matches
- Export matches to various formats
- Additional URL resolution sources beyond Twitter bios

### Migration Notes

**For existing deployments**:
1. No database schema changes required
2. Existing `url_resolution_queue` table is used as-is
3. Scripts in `scripts/` folder remain unchanged and functional
4. New ProfileMatching module is additive, doesn't break existing workflows

**To use new features**:
1. Use ProfileMatching/main.py instead of individual scripts
2. Or run scripts directly from ProfileMatching folder
3. Or update import paths to use ProfileMatching module