Microsoft Azure AI Fundamentals (AI-900)Describe fundamental principles of machine learning on AzureMedium
A team of data scientists is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on their past purchase history and browsing behavior, as well as the behavior of similar users. This type of recommendation system, which analyzes relationships between users and items, is commonly known as what?
- AHybrid Recommendation
- BCollaborative Filtering
- CKnowledge-Based Filtering
- DContent-Based Filtering
Show answer & explanationAnswer & explanation
Correct answer: B. Collaborative Filtering
Collaborative filtering is a common technique for recommendation systems that works by collecting preferences or taste information from many users (collaborating) and uses this to predict what a user might like based on the preferences of similar users.
Why the other options are wrong
- A. Hybrid recommendation combines multiple approaches, but collaborative filtering is the core concept described.
- C. Knowledge-based filtering relies on explicit domain knowledge and user preferences, not user/item relationships.
- D. Content-based filtering recommends items similar to those a user liked in the past, based on item attributes.
Collaborative Filtering
Collaborative filtering is a technique used by recommendation systems that makes predictions about a user's interests by collecting preferences from many users. It identifies users with similar tastes and recommends items liked by those 'similar' users.
- Relies on user-item interaction data (ratings, purchases, views).
- Two main types: user-based and item-based.
- Suffers from 'cold start' problem for new users/items.
Memory trick: Collaborative: Users 'collaborate' to help each other discover new things.