Personalization Signals for "For You" Recommendations

Purpose

This document outlines how we personalize the "For You" recommendations for each user. We use a weighted signal system that learns from user behavior to surface the most relevant courses and consultations.

How It Works

Instead of calculating personalization on every request, we:

1. Pre-compute a user interest profile (embedding) from their activity
2. Store this profile and update it when behavior changes
3. Match the user profile against product profiles using vector similarity
4. Serve personalized recommendations instantly

This approach is efficient - we only recalculate when the user's preferences actually change.

Signal Categories

Strong Intent Signals (Highest Impact)

These indicate direct purchase or learning intent:

Signal	Weight	Description
Wishlist Items	5.0	Products the user explicitly saved for later
Cart Items	4.0	Products added to cart (even if abandoned)
Enrolled Products	3.0	Categories the user has already invested in

Explicit Preferences (User-Declared)

What users tell us about themselves:

Signal	Weight	Source
Primary Interest Category	3.0	Student profile setup
Other Interests	2.0	Secondary interests in profile
Learning Objectives	2.0	Goals stated in profile

Behavioral Signals (Implicit)

What we learn from user actions:

Signal	Weight	Notes
Recent Views	2.0	Products viewed in last 7-30 days (decays over time)
Search Queries	1.5	Keywords and categories from search history
High Ratings	1.5	Products rated 4-5 stars by the user
Engagement Time	1.0	Categories where user spends the most time

Negative Signals (Exclusions)

What we avoid recommending:

Signal	Effect	Rationale
Already Enrolled	Excluded	Never recommend what they already have
Low Ratings (1-2 stars)	Deprioritized	User showed dissatisfaction with similar content

When Recommendations Update

We recalculate a user's profile when their preferences change:

Event	Update Timing	Why
User enrolls in course/consultation	Immediate	Strong signal - they paid for it
User adds to wishlist	Within 1 minute	Shows clear interest
User removes from wishlist	Within 1 minute	Preferences changed
User adds to cart	Within 5 minutes	Purchase intent (might checkout)
User gives 4-5 star review	Immediate	Positive affinity
User gives 1-2 star review	Immediate	Negative signal to incorporate
User updates profile interests	Immediate	Explicit preference change

Cold Start (New Users)

For users without activity history:

1. Use their Primary Interest Category from profile setup
2. If no profile yet → show Trending/Popular content
3. Build personalized recommendations after first interactions

Data Sources

Data	Where It Lives	What We Use
Wishlist	wishlist table	Product IDs, timestamps
Cart	cart_items table	Product IDs
Enrollments	enrollment table	Course/consultation IDs, categories
Views	impression table	Entity IDs, view counts, recency
Reviews	review table	Ratings, product associations
Profile	student table	Interest category, objectives, skills
Search	Elasticsearch logs	Query terms, filters used

Key Decisions

• Event-driven updates only: We don't recalculate on a schedule. If a user hasn't interacted, their preferences haven't changed.
• Weighted averaging: Stronger signals (wishlist, cart) influence recommendations more than weak signals (views).
• Recency matters: Recent views weighted higher than old ones (decay function).
• Negative signals excluded: We never recommend already-purchased items.

Success Metrics

• Click-through rate on "For You" section
• Conversion rate from recommendations
• Wishlist additions from recommendations
• User engagement with personalized vs non-personalized content

Technical Implementation

See: docs/flows/ for integration details (if created)

Key files:

• server/src/recommendation/services/user-embedding.service.ts - Signal collection and embedding calculation
• server/src/recommendation/listeners/user-embedding.listener.ts - Event handlers for updates
• server/src/recommendation/processors/user-embedding.processor.ts - Background job processing

Last Updated

2025-01-27