- 2074 Magh · 4+4 marks
Recommender system design and types
Short answer
Recommender design requires accuracy, scalability, sparsity handling, and real-time personalization. Types include content-based (item similarity), collaborative filtering (user similarity), hybrid, knowledge-based (explicit rules), demographic, popularity-based, and context-aware systems.
Describe the characteristics for the design of the typical recommender system. Explain the different types of recommender systems.
Characteristics for the design of a typical recommender system
- Data collection: explicit (ratings, reviews) and implicit (clicks, purchases, viewing time) feedback, plus user profiles and item attributes.
- Accuracy and relevance: predictions must match user taste.
- Scalability: handle millions of users and items in real time.
- Handling sparsity and cold start for new users and items.
- Diversity, novelty and serendipity: avoid showing only the obvious.
- Personalisation and context awareness: time, place and device.
- Real-time response and updating with new data.
- Robustness and security: resist fake ratings and attacks.
- Privacy: protect user data.
- Explainability and trust: tell why an item is recommended.
- Evaluation: precision, recall, RMSE and user satisfaction.
Types of recommender systems
| Type | Principle | Example |
|---|---|---|
| Content-based | Recommends items similar to those the user liked, using item features | News with similar topics |
| Collaborative filtering | Uses preferences of similar users or items | Amazon "customers also bought" |
| Hybrid | Combines two or more methods to reduce weaknesses | Netflix |
| Knowledge-based | Uses explicit requirements and domain rules | Choosing a laptop by budget and RAM |
| Demographic | Uses age, gender, location | Recommending local products |
| Popularity-based | Recommends most popular items | Top 10 lists |
| Context-aware | Considers time, location, mood | Restaurants nearby at lunch |
✓ Answer checked by Claude Opus 5.5 (high effort)
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