---
title: FomoFeed User Profiler
emoji: 👤
colorFrom: indigo
colorTo: green
sdk: docker
pinned: false
---

# FomoFeed User Profiler AI

AI-powered user profiling using Turkish BERT embeddings and behavioral analysis.

## 🎯 Purpose
Creates comprehensive user profiles based on content consumption, creation, and engagement patterns.

## 🧠 Model
- BERT: `dbmdz/bert-base-turkish-cased` (768-dim embeddings)
- Analysis: Rule-based behavioral classification
- Output: Semantic embeddings + structured profile

## 📥 Input
```json
{
  "user_id": 12,
  "post_captions": ["Harika bir gün", "Yeni proje"],
  "moment_captions": ["Kahve molası"],
  "liked_tags": ["yazilim", "teknoloji", "kahve"],
  "saved_tags": ["yazilim", "python"],
  "commented_tags": ["teknoloji"],
  "engagement_hours": [9, 12, 19, 20, 21],
  "engagement_types": ["view", "like", "save", "comment"]
}
```

## 📤 Output
```json
{
  "user_id": 12,
  "interests": ["yazilim", "teknoloji", "python"],
  "content_preference": {
    "passive_consumer": 0.25,
    "active_engager": 0.60,
    "content_creator": 0.15
  },
  "activity_pattern": {
    "peak_hours": [19, 20, 21],
    "activity_distribution": {
      "morning": 0.2,
      "afternoon": 0.3,
      "evening": 0.4,
      "night": 0.1
    },
    "timezone_pattern": "evening"
  },
  "engagement_style": {
    "style": "active",
    "interaction_rate": 0.45,
    "content_saver": true,
    "commenter": true
  },
  "optimal_hours": [19, 20, 21],
  "confidence": 0.78
}
```

## 🚀 Usage

### Create Profile
```bash
curl -X POST "https://YOUR-SPACE-URL/profile" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": 12,
    "liked_tags": ["tech", "ai"],
    "engagement_hours": [19, 20, 21],
    "engagement_types": ["like", "save"]
  }'
```

### Get BERT Embedding
```bash
curl -X POST "https://YOUR-SPACE-URL/embedding" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": 12,
    "post_captions": ["AI is amazing"],
    "liked_tags": ["ai", "tech"]
  }'
```

## 📊 Profile Dimensions

### Interests
Top 10 tags based on weighted engagement (saves > comments > likes)

### Content Preference
- **Passive Consumer**: High view ratio
- **Active Engager**: High like/save ratio
- **Content Creator**: Post/moment creation frequency

### Activity Pattern
- **Peak Hours**: Top 3 most active hours
- **Distribution**: Morning/afternoon/evening/night breakdown
- **Timezone Pattern**: Primary activity window

### Engagement Style
- **Lurker**: <10% interaction rate
- **Casual**: 10-30% interaction rate
- **Active**: 30-60% interaction rate
- **Power User**: >60% interaction rate

## 🎯 Use Cases
- Content recommendation
- Feed personalization
- Notification timing
- Creator matching
- Trend prediction

## 📈 Performance
- Profile creation: <200ms
- BERT embedding: ~500ms
- Batch support: Up to 50 users
- Confidence: 0.0-1.0 (based on data volume)

Built with FastAPI + Transformers + PyTorch