Collaborative Filtering and Recommendation Systems

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In collaborative filtering, what is the goal of recommending items to customers?

To recommend items randomly to increase diversity

To recommend items that have previously been purchased by similar customers (correct)

To recommend items based on their popularity

To recommend items based on users who are most different

What similarity measure does Amazon use in generating recommendations?

Pearson correlation

Euclidean distance

Cosine similarity (correct)

Jaccard index

What kind of data does Amazon use to generate recommendations?

Only product features

Only user demographic data

Both user demographic data and product features

Only user interaction data without looking at features (correct)

Which users are considered more similar in collaborative filtering?

Users who have rated some different items but also rated the same items Signup and view all the answers

What is the binary representation of items purchased by user u?

R*. Signup and view all the answers

In the context of defining similarity between users and items, what does 𝑅.,+ represent?

Item representation (I) Signup and view all the answers

How is the best case scenario defined for similarity scores in terms of Euclidean distance?

When users i and j buy the same item Signup and view all the answers

What defines the Euclidean distance between two items i and j in this context?

R*,+ - R., Signup and view all the answers

What is the purpose of subtracting the average rating by user v from each rating in the formula for Pearson correlation similarity between users?

To eliminate the effect of below-average ratings on the similarity calculation Signup and view all the answers

How does the cosine similarity differ from Pearson correlation similarity in terms of item consideration?

Cosine similarity considers all items while Pearson correlation only focuses on shared items. Signup and view all the answers

Why is it important to consider only shared items when determining similarity between users based on ratings?

To ensure users are maximally similar if they rate shared items the same way Signup and view all the answers

In the context of recommendation systems, what is the purpose of Euclidean distance in relation to similarities between items?

To measure the closeness or similarity between items based on their ratings Signup and view all the answers

Collaborative Filtering and Recommendation Systems

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Questions and Answers

In collaborative filtering, what is the goal of recommending items to customers?

What similarity measure does Amazon use in generating recommendations?

What kind of data does Amazon use to generate recommendations?

Which users are considered more similar in collaborative filtering?

What is the binary representation of items purchased by user u?

In the context of defining similarity between users and items, what does 𝑅.,+ represent?

How is the best case scenario defined for similarity scores in terms of Euclidean distance?

What defines the Euclidean distance between two items i and j in this context?

What is the purpose of subtracting the average rating by user v from each rating in the formula for Pearson correlation similarity between users?

How does the cosine similarity differ from Pearson correlation similarity in terms of item consideration?

Why is it important to consider only shared items when determining similarity between users based on ratings?

In the context of recommendation systems, what is the purpose of Euclidean distance in relation to similarities between items?

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