Predictive Analytics with TensorFlow 10.1: Recommendation Systems

The video tutorial covers recommendation systems, focusing on collaborative, content-based, and hybrid approaches. It discusses the challenges of collaborative filtering, such as cold start, scalability, and sparsity, and explains content-based filtering's reliance on item characteristics and user preferences. The tutorial introduces hybrid systems that combine both methods for improved accuracy. It also covers the utility matrix, data preparation using the MovieLens dataset, and building a recommendation model using TensorFlow, SVD, and K-means clustering.

What is a major challenge faced by collaborative filtering methods?

• Low computational power

• Overfitting

• Cold start problem

• High accuracy

Which approach uses item characteristics to recommend similar items?

• Hybrid filtering

• Content-based filtering

• Personality-based approach

• Collaborative filtering

What is a key advantage of hybrid recommendation systems?

• They are simpler to implement

• They combine the strengths of multiple approaches

• They require less data

• They are faster to compute

What is the purpose of the utility matrix in recommendation systems?

• To track user login times

• To list all available items

• To calculate the average rating of items

• To store user preferences for items

What algorithm is used in the movie recommendation engine for collaborative filtering?

• Linear Regression

• Singular Value Decomposition

• K-means clustering

• Principal Component Analysis

Which dataset is used for developing the movie recommendation engine?

• Amazon dataset

• MovieLens dataset

• IMDB dataset

• Netflix dataset

What is the role of K-means clustering in the recommendation engine?

• To predict user ratings

• To cluster similar movies

• To filter out unpopular movies

• To enhance user profiles

What is the significance of setting a random seed in model training?

• To increase model accuracy

• To ensure reproducibility

• To reduce overfitting

• To speed up training

How does the prediction method in the recommendation engine work?

• It evaluates tensors to predict ratings

• It uses user feedback to adjust recommendations

• It clusters users based on preferences

• It filters out low-rated items

What is the purpose of using PCA in the recommendation engine?

• To increase the number of features

• To reduce dimensionality

• To enhance user profiles

• To improve computational speed