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 fun
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.
This is a possible scenario of users, items and ratings; Compute the similarity between item 1 and 2
2.15
0.788
0.987
-10
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
The content based used more to :
Recommend products
Recommend movies
Recommend articles
Recommend music
hybrid recommender a recommender that combines two or more algorithms into a single recommender
Yes
Ala
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