Recommender Systems with Machine Learning - User-Based Collaborative Filtering

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Information Technology (IT), Architecture, Social Studies
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University
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Hard
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5 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why does the course not cover the implementation of user-based collaborative filtering in detail?
Because it is not relevant to recommendation systems.
Because it is not a part of machine learning.
Because item-based and content-based filtering have already been covered.
Because it is too complex to understand.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the first step in user-based collaborative filtering?
Data preparation.
Testing the recommendation engine.
Implementing deep learning models.
Using K nearest neighbors.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the example of the book dataset, what was done after combining the data frames?
The data was used to create a histogram.
The data was used to perform a machine learning algorithm.
A new dataset was created.
The data was discarded.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which techniques are typically used in user-based collaborative filtering instead of K nearest neighbors?
Co-clustering, baseline, and normal predictor.
Decision trees and random forests.
Linear regression and logistic regression.
Support vector machines and neural networks.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the final step in developing a user-based collaborative filtering system?
Using K nearest neighbors.
Data preparation.
Testing the recommendation engine.
Combining data frames.
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