AI Recommendation Systems: Show Users What They Want
35% of Amazon purchases come from recommendations. What about yours?
Recommendation Approaches
Collaborative Filtering
- “Users like you also bought…”
- Based on behavior patterns
- Cold start challenge
- Requires user data
Content-Based
- “Similar to what you viewed…”
- Based on item attributes
- Works for new users
- Limited discovery
Hybrid
- Combines approaches
- Best of both worlds
- More complex
- Industry standard
Impact
| Industry | Revenue Impact |
|---|---|
| E-commerce | +10-30% |
| Streaming | +60% engagement |
| News | +40% clicks |
| SaaS | +25% adoption |
Use Cases
| Application | Recommendation Type |
|---|---|
| Products | Purchase history |
| Content | Viewing behavior |
| Music | Listening patterns |
| Jobs | Skills matching |
Tools
| Tool | Focus |
|---|---|
| Amazon Personalize | AWS |
| Recombee | API-first |
| Dynamic Yield | Marketing |
| Algolia Recommend | Search + recs |
Quick Wins
- Recently viewed - Simple, effective
- Bestsellers - Social proof
- Frequently bought together - Cross-sell
- Personalized home - Increase engagement
Want better recommendations for your users? Let’s discuss your personalization needs.