Smart Recommendation Engine - AI Examples
AI recommendation systems for e-commerce, content, B2B. Collaborative filtering, hybrid approaches. Practical cases.
Recommendation systems combine collaborative filtering, content-based and recent LLM approaches for personalisation. From product recommendations to content discovery.
E-commerce product recommendations
A webshop used a hybrid model: collaborative filtering for "customers who bought X" and content-based for cold start. Conversion increased 25%.
- Hybrid model
- Cold start handling
- Real-time scoring
B2B content recommendations
A professional SaaS used embeddings to find similar articles and resources. Engagement and time-on-site increased.
- Semantic embeddings
- User behaviour signals
- Diversity in results
Key takeaways
- Hybrid approaches work better than pure collaborative or content-based.
- Cold start is solvable with content features or popular items.
- Measure impact via A/B tests and engagement metrics.
How AVARC Solutions can help
AVARC Solutions builds recommendation engines: from classical ML to embedding-based. We integrate with your product catalog and user data.
Frequently asked questions
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