Lesson 63 - Recommendation System

Lesson 63 - Recommendation System

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Indira Indira

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8 questions

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1.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Which process/system is used behind the spotify new song suggestion?

Recommendation System

Semantic Analysis

Natural Language Processing

Pattern Recogniton

2.

MULTIPLE SELECT QUESTION

2 mins • 1 pt

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

NetFlix

YouTube

Amazon

Google Classroom

3.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Which filtering uses knowledge of each product to recommend a similar product?

Content Based Recommender

Collaborate Filtering

4.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Which filtering uses knowledge of user’s past purchase/selection ?

Content Based Recommender

Collaborate Filtering

5.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Recommender systems can be defined as

Systems that evaluate quality based on the preferences of others with a similar point of view

Systems that evaluate quality based on the purchase history of any

particular person only

Systems that evaluate quality based on the demand of items

Systems that evaluate quality based on the association rule mining

techniques

6.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Identify the correct recommendation system's algorithm(s) from

given options.

Collaborative page ranking

Item based recommendation system

Collaborative filtering

Object based recommendation system

7.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Identify the correct statements related to Collaborative Filtering

The problem of collaborative filtering is to predict how well a user will like an item that he has not rated given a set of historical preference judgments for a community of users.

Predict the opinion the user will have on the different items

Recommend the ‘best’ items based on the user’s previous likings and the opinions of like-minded users whose ratings are similar

All the listed options

8.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

User-Based Collaborative Filtering methods -

Are based on user's similarity only

In such methods complexity grows linearly with the number of customers and items

Suffers the problem of sparsity of recommendations on the data set

based Item selection Method