Recommender Systems: An Applied Approach using Deep Learning - Making Recommendations

Recommender Systems: An Applied Approach using Deep Learning - Making Recommendations

Assessment

Interactive Video

Computers

11th - 12th Grade

Hard

Created by

Wayground Content

FREE Resource

The video tutorial explains how to build a recommendation system using TensorFlow Recommenders. It covers the use of a brute force algorithm for indexing, creating an index from a dataset, and making predictions for users. The tutorial concludes with a recap of the steps involved in building the model and highlights the importance of deep learning in recommendation systems.

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7 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What algorithm is used for testing the recommendation system in this tutorial?

Collaborative Filtering

Matrix Factorization

Brute Force

Content-Based Filtering

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary input for the brute force algorithm in this context?

User Model

Item Model

Recommendation List

Dataset Batch

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of mapping the dataset with beer names?

To filter out unwanted data

To organize data for indexing

To enhance the prediction accuracy

To create a user profile

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is the username converted for making recommendations?

As a NumPy array

As a Pandas DataFrame

As a Python list

As a TensorFlow constant

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What factors influence the recommendations made to a user?

User's location

User's previous choices and likes

Random selection

Most popular items

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is emphasized as the best way to make recommendation systems?

Using collaborative filtering

Using deep learning

Using content-based filtering

Using hybrid methods

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the first step in the process of building a recommendation system as per the recap?

Mapping beer names

Installing TensorFlow Recommenders

Creating data frames

Splitting data into training and testing

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