Recommender Systems with Machine Learning - User-Based Collaborative Filtering

The video discusses user-based collaborative filtering, explaining why code implementation is not covered due to prior coverage of item-based and content-based filtering. It emphasizes the transition to deep learning for recommendation systems. The steps in user-based collaborative filtering are outlined, focusing on data preparation and insights. Techniques like Co clustering and baseline predictors are introduced, concluding with testing the recommendation engine.

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.

What is the first step in user-based collaborative filtering?

• Data preparation.

• Testing the recommendation engine.

• Implementing deep learning models.

• Using K nearest neighbors.

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.

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.

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.