Recommendation Systems Flashcards
What are some ways you can address the cold-start problem that can occur for new users of a collaborative filter recommendation system? There could be more than one answer.
Give up and ask new users to make their own recommendations.
Ask the new user’s friends to recommend items they think would be relevant.
Rely on a content-based method instead for new users.
Ask the user for some basic preferences.
Rely on a content-based method instead for new users.
Ask the user for some basic preferences.
Suppose you want to build a collaborative filter to suggest new hiking trails for users. The problem is you don’t have any good explicit user ratings for trails. What feature might be useful for creating an implicit measure of a user’s rating for a trail instead?
The distance of the trail to the user’s home.
The length of the trail.
The number of times all users hiked that trail.
The number of times the user hiked that trail
The number of times the user hiked that trail
When building a content-based recommender system, it’s important to express both your items and users using the same embedding space (that is. the same dimensions and features).
True or False
True
Choose the correct three answers. What are some potential techniques to determine how similar two items are? There could be more than one answer.
Compare the norms (which are directionless) of the two items in an embedding space to see if they are similar.
Compute the inner product between the two items in an embedding space.
Plot the two items in the embedding space and simply visually inspect to see how close they are on a graph by looking briefly.
Count how many features the two items have in common.
Measure the cosine similarity between the two items in an embedding space.
Compute the inner product between the two items in an embedding space.
Count how many features the two items have in common.
Measure the cosine similarity between the two items in an embedding space.
ALS and WALS create embedding tables for both users and items. Because these are held in memory, it’s important to plan for their size. How big would you expect the embedding table for the users to be?
Proportional to k, the number of dimensions in your embedding space.
Proportional to the number of users.
Proportional to the number of users multiplied by the number of items.
Proportional to the number of users squared.
Proportional to k, the number of dimensions in your embedding space.
Proportional to the number of users.
