Recommender System

Test Your Recommender System Knowledge

Welcome to the Recommender System Quiz! This quiz is designed for enthusiasts and professionals alike who want to deepen their understanding of recommender systems and their functionalities.

In this quiz, you will:

Explore various types of recommender systems.
Test your knowledge of data collection methods.
Learn about collaborative and content-based filtering.
Assess your familiarity with hybrid recommender systems.

9 Questions2 MinutesCreated by AnalyzingData12

Recommender systems can be defined as:

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 preferences of others with a similar point of view

None of these

Identify the correct statements, which are used to collect the data for recommender systems:

Asking a user to rate an item on a sliding scale.

Asking a user to rank a collection of items from favorite to least favorite

Presenting two items to a user and asking him/her to choose the better one of them.

Asking a user to create a list of items that he/she likes

Identify the correct statements related to Collaborative Filtering:

Cannot predict the opinion the user will have on the different items

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.

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

None of these

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

It is more scalable than Item-Based Collaborative Filtering methods

Suffers the problem of sparsity of recommendations on the data set

Item-Based Collaborative Filtering methods

Are based on user's similarity

It is more scalable than User-Based Collaborative Filtering methods

Scales independently of the catalog size or the total number of customers

None of these

The content based methods:

It may use, same author, artist, director, or similar keywords/subjects

It is impractical to base a query on all the items in content based method

Given the user’s purchased and rated items, constructs a search query to find other popular items

None of these

Recommender System

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