User-based vs item-based in ML Python - Quick Revision & Key Differences

Recall & Review

beginner

What is user-based collaborative filtering?

User-based collaborative filtering recommends items to a user by finding other users with similar tastes and suggesting items they liked.

beginner

What is item-based collaborative filtering?

Item-based collaborative filtering recommends items similar to those a user has liked before, based on item-to-item similarity.

intermediate

Which method is usually more scalable: user-based or item-based filtering?

Item-based filtering is usually more scalable because the number of items is often smaller and more stable than the number of users.

intermediate

How does user similarity get calculated in user-based filtering?

User similarity is often calculated using measures like cosine similarity or Pearson correlation on their item ratings or interactions.

intermediate

Why might item-based filtering provide more stable recommendations over time?

Because item relationships change less frequently than user preferences, item-based filtering tends to give more stable recommendations.

What does user-based collaborative filtering primarily rely on?

A Finding similar users

B Finding similar items

C Random item selection

D Content features of items

User-based filtering finds users with similar tastes to recommend items.

Which filtering method compares items to recommend new ones?

A Hybrid filtering

B User-based filtering

C Item-based filtering

D Content-based filtering

Item-based filtering compares items to find similar ones for recommendation.

Which similarity measure is commonly used in collaborative filtering?

A Euclidean distance

B Cosine similarity

C Jaccard index

D Manhattan distance

Cosine similarity is commonly used to measure similarity between users or items.

Why is item-based filtering often preferred for large datasets?

A Items are fewer and more stable than users

B Users are fewer than items

C It requires no similarity calculations

D It uses random recommendations

Item-based filtering scales better because items are usually fewer and change less often.

Which filtering method might struggle if a new user has no history?

A Item-based filtering

B Neither

C Both user-based and item-based filtering

D User-based filtering

User-based filtering needs user history to find similar users, so it struggles with new users.

Explain the main difference between user-based and item-based collaborative filtering.

Think about whether the system looks for similar users or similar items.

Describe why item-based filtering can be more scalable than user-based filtering.

Consider which changes less often: users or items.