Content-based filtering in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is content-based filtering in recommendation systems?

Content-based filtering recommends items to a user based on the features of items the user liked before. It looks at item details and matches similar items to the user's preferences.

beginner

How does content-based filtering use item features?

It creates a profile of the user's preferences by analyzing features of items they liked, such as genre, keywords, or attributes, then finds new items with similar features to recommend.

intermediate

What is a common way to measure similarity between items in content-based filtering?

Cosine similarity is often used to measure how close two items are based on their feature vectors. It calculates the angle between two vectors to find similarity.

intermediate

What is a limitation of content-based filtering?

It can only recommend items similar to what the user already knows and likes, so it may not suggest diverse or novel items outside the user's past preferences.

intermediate

Explain how a user profile is built in content-based filtering.

A user profile is built by collecting features from items the user liked and combining them, often by averaging or weighting features, to represent the user's taste for future recommendations.

What does content-based filtering primarily rely on to make recommendations?

A Features of items the user liked

B Ratings from other users

C Random item selection

D Popularity of items

Content-based filtering uses the features of items the user liked to find similar items.

Which similarity measure is commonly used in content-based filtering?

A Euclidean distance

B Manhattan distance

C Jaccard index

D Cosine similarity

Cosine similarity measures the angle between feature vectors and is commonly used to find similar items.

What is a key limitation of content-based filtering?

A It ignores item features

B It requires many users' data

C It cannot recommend new or diverse items

D It always recommends popular items

Content-based filtering tends to recommend items similar to what the user already liked, limiting diversity.

How is a user profile created in content-based filtering?

A By averaging features of liked items

B By counting user clicks

C By using other users' preferences

D By random sampling

User profiles are built by combining features of items the user liked, often by averaging.

Content-based filtering is best described as:

A Using social network data

B Using item features to recommend similar items

C Recommending items based on popularity

D Using collaborative user ratings

Content-based filtering recommends items similar to those the user liked based on item features.

Describe how content-based filtering creates recommendations for a user.

Think about how the system learns what the user likes and finds items like those.

What are the main advantages and disadvantages of content-based filtering?

Consider what it does well and where it struggles.