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
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