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Recommendation System

Total questions: 10

Worksheet time: 6mins

Name
Class
Date
1.

Select the examples, where Recommendation Algorithm works? [Multiple Answers]

a)

Google Classroom

b)

Amazon

c)

YouTube

d)

NetFlix

2.

Which of the following businesses would be least likely to use Recommendation Engine?

a)

Facebook

b)

Ola

c)

WhatsApp

d)

Instagram

3.

Explicit data is something which ecommerce websites and other businesses specifically ask from the users.

a)

TRUE

b)

FALSE

4.

implicit data is collected through users' behaviour on the website.

a)

TRUE

b)

FALSE

5.

For an ecommerce website, which of the following is explicit data?

a)

Amazon's Star rating system

b)

Page views

c)

Thumbs up/Thumbs down

d)

No. of clicks on a page/website

6.

Which of the following are not used for filtering in a Recommendation Engine?

a)

Sine Similarity

b)

Cosine Similarity

c)

Jaccard Similarity

d)

Euclidean Distance

7.

Which of the below is collaborative filtering based Recommendation Engine?

a)

Items inspired by your history

b)

Items which customers like you, viewed earlier

8.

What kind of information does a Recommendation Engine need for effective recommendations?

a)

Users' explicit interactions such as information about their past activity, ratings, reviews

b)

Users’ implicit interactions such as device they use for access, clicks on a link, location, and dates

c)

Other information about profile, such as gender, age, or income levels

d)

All of the above

9.

Why are recommendation engines becoming popular?

a)

It is mandatory to have recommendation engine as per telecom rules

b)

Users have lesser time, more options and face an information overload

c)

It is better to recommend than ask user to search on mobile phones

d)

Users don't know what they want

10.

_________ methods work fine for thousands of users, but ________ gets to be a problem when we have millions of users.

a)

User-based recommendation, Scalability

b)

item-based recommendation, Scalability

c)

item-based recommendation, Sparsity