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backend/services/interest_profiling_service.py
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backend/services/interest_profiling_service.py
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"""
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User Interest Profiling Service for Munich News Daily.
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Builds and maintains user interest profiles based on article click behavior.
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"""
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional
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from database import link_clicks_collection
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from pymongo import MongoClient
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from config import Config
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# Connect to MongoDB
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client = MongoClient(Config.MONGODB_URI)
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db = client[Config.DB_NAME]
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user_interests_collection = db['user_interests']
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def update_user_interests(subscriber_email: str, keywords: List[str], category: str) -> Dict:
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"""
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Update user interest profile based on a clicked article.
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Increments interest scores for the article's keywords and category.
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Creates a new profile if the user doesn't have one yet.
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Args:
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subscriber_email: Email address of the user
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keywords: List of keywords from the clicked article
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category: Category of the clicked article
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Returns:
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dict: Updated user interest profile
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"""
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current_time = datetime.utcnow()
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# Get existing profile or create new one
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profile = user_interests_collection.find_one({'email': subscriber_email})
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if not profile:
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# Create new profile
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profile = {
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'email': subscriber_email,
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'categories': {},
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'keywords': {},
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'total_clicks': 0,
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'last_updated': current_time,
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'created_at': current_time
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}
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# Update category interest (increment by 0.1, max 1.0)
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current_category_score = profile['categories'].get(category, 0.0)
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profile['categories'][category] = min(current_category_score + 0.1, 1.0)
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# Update keyword interests (increment by 0.1, max 1.0)
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for keyword in keywords:
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if keyword: # Skip empty keywords
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current_keyword_score = profile['keywords'].get(keyword, 0.0)
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profile['keywords'][keyword] = min(current_keyword_score + 0.1, 1.0)
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# Update metadata
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profile['total_clicks'] = profile.get('total_clicks', 0) + 1
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profile['last_updated'] = current_time
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# Upsert profile
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user_interests_collection.update_one(
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{'email': subscriber_email},
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{'$set': profile},
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upsert=True
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)
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return profile
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def get_user_interests(subscriber_email: str) -> Optional[Dict]:
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"""
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Get user interest profile.
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Args:
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subscriber_email: Email address of the user
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Returns:
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dict: User interest profile or None if not found
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"""
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return user_interests_collection.find_one({'email': subscriber_email})
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def decay_user_interests(decay_factor: float = 0.95, days_threshold: int = 7) -> Dict[str, int]:
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"""
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Decay interest scores for users who haven't clicked recently.
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Reduces interest scores over time to reflect changing interests.
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Only decays profiles that haven't been updated in the last N days.
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Args:
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decay_factor: Multiplier for interest scores (default: 0.95 = 5% decay)
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days_threshold: Only decay profiles older than this many days (default: 7)
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Returns:
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dict: Statistics about the decay operation
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- profiles_decayed: Number of profiles that were decayed
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- profiles_checked: Total number of profiles checked
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"""
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cutoff_date = datetime.utcnow() - timedelta(days=days_threshold)
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# Find profiles that haven't been updated recently
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old_profiles = user_interests_collection.find({
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'last_updated': {'$lt': cutoff_date}
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})
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profiles_decayed = 0
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profiles_checked = 0
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for profile in old_profiles:
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profiles_checked += 1
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# Decay category scores
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decayed_categories = {}
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for category, score in profile.get('categories', {}).items():
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new_score = score * decay_factor
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# Remove categories with very low scores (< 0.05)
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if new_score >= 0.05:
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decayed_categories[category] = round(new_score, 3)
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# Decay keyword scores
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decayed_keywords = {}
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for keyword, score in profile.get('keywords', {}).items():
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new_score = score * decay_factor
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# Remove keywords with very low scores (< 0.05)
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if new_score >= 0.05:
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decayed_keywords[keyword] = round(new_score, 3)
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# Update profile with decayed scores
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user_interests_collection.update_one(
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{'email': profile['email']},
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{
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'$set': {
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'categories': decayed_categories,
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'keywords': decayed_keywords,
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'last_decayed': datetime.utcnow()
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}
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}
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)
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profiles_decayed += 1
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return {
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'profiles_decayed': profiles_decayed,
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'profiles_checked': profiles_checked
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}
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def get_top_interests(subscriber_email: str, top_n: int = 10) -> Dict[str, List[tuple]]:
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"""
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Get user's top interests sorted by score.
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Args:
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subscriber_email: Email address of the user
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top_n: Number of top interests to return (default: 10)
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Returns:
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dict: Top interests containing:
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- top_categories: List of (category, score) tuples
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- top_keywords: List of (keyword, score) tuples
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"""
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profile = get_user_interests(subscriber_email)
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if not profile:
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return {
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'top_categories': [],
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'top_keywords': []
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}
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# Sort categories by score
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categories = profile.get('categories', {})
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top_categories = sorted(categories.items(), key=lambda x: x[1], reverse=True)[:top_n]
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# Sort keywords by score
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keywords = profile.get('keywords', {})
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top_keywords = sorted(keywords.items(), key=lambda x: x[1], reverse=True)[:top_n]
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return {
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'top_categories': top_categories,
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'top_keywords': top_keywords
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}
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def build_interests_from_history(subscriber_email: str, days_lookback: int = 30) -> Dict:
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"""
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Build or rebuild user interest profile from click history.
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Useful for:
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- Initializing profiles for existing users
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- Rebuilding profiles after algorithm changes
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- Backfilling data
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Args:
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subscriber_email: Email address of the user
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days_lookback: Number of days of history to analyze (default: 30)
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Returns:
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dict: Newly built interest profile
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"""
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cutoff_date = datetime.utcnow() - timedelta(days=days_lookback)
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# Get all clicks from this user in the lookback period
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clicks = link_clicks_collection.find({
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'subscriber_email': subscriber_email,
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'clicked': True,
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'clicked_at': {'$gte': cutoff_date}
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})
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# Initialize profile
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profile = {
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'email': subscriber_email,
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'categories': {},
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'keywords': {},
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'total_clicks': 0,
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'last_updated': datetime.utcnow(),
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'created_at': datetime.utcnow()
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}
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# Process each click
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for click in clicks:
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category = click.get('category', 'general')
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keywords = click.get('keywords', [])
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# Update category score
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profile['categories'][category] = profile['categories'].get(category, 0.0) + 0.1
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# Update keyword scores
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for keyword in keywords:
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if keyword:
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profile['keywords'][keyword] = profile['keywords'].get(keyword, 0.0) + 0.1
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profile['total_clicks'] += 1
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# Cap scores at 1.0
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for category in profile['categories']:
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profile['categories'][category] = min(profile['categories'][category], 1.0)
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for keyword in profile['keywords']:
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profile['keywords'][keyword] = min(profile['keywords'][keyword], 1.0)
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# Save profile
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if profile['total_clicks'] > 0:
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user_interests_collection.update_one(
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{'email': subscriber_email},
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{'$set': profile},
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upsert=True
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)
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return profile
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def get_interest_statistics() -> Dict:
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"""
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Get statistics about user interests across all users.
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Returns:
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dict: Statistics containing:
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- total_users: Total number of users with profiles
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- avg_clicks_per_user: Average number of clicks per user
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- most_popular_categories: Top categories across all users
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- most_popular_keywords: Top keywords across all users
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"""
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total_users = user_interests_collection.count_documents({})
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if total_users == 0:
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return {
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'total_users': 0,
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'avg_clicks_per_user': 0,
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'most_popular_categories': [],
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'most_popular_keywords': []
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}
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# Calculate average clicks
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pipeline = [
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{
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'$group': {
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'_id': None,
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'total_clicks': {'$sum': '$total_clicks'}
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}
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}
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]
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result = list(user_interests_collection.aggregate(pipeline))
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total_clicks = result[0]['total_clicks'] if result else 0
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avg_clicks = total_clicks / total_users if total_users > 0 else 0
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# Get most popular categories
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category_counts = {}
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keyword_counts = {}
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for profile in user_interests_collection.find({}):
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for category, score in profile.get('categories', {}).items():
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category_counts[category] = category_counts.get(category, 0) + score
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for keyword, score in profile.get('keywords', {}).items():
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keyword_counts[keyword] = keyword_counts.get(keyword, 0) + score
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# Sort and get top 10
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top_categories = sorted(category_counts.items(), key=lambda x: x[1], reverse=True)[:10]
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top_keywords = sorted(keyword_counts.items(), key=lambda x: x[1], reverse=True)[:10]
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return {
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'total_users': total_users,
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'avg_clicks_per_user': round(avg_clicks, 2),
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'most_popular_categories': top_categories,
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'most_popular_keywords': top_keywords
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}
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def delete_user_interests(subscriber_email: str) -> bool:
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"""
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Delete user interest profile (for GDPR compliance).
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Args:
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subscriber_email: Email address of the user
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Returns:
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bool: True if profile was deleted, False if not found
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"""
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result = user_interests_collection.delete_one({'email': subscriber_email})
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return result.deleted_count > 0
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