Worksheets[HTTTDN] Recommender System Summarize Quizz
Total questions: 15
Worksheet time: 9mins
What is a Recommender System?
A system that predicts future interests of users based on past behavior.
A system for inventory management.
A system for calculating sales tax.
None of the above.
Which of the following is NOT a type of Recommender System?
Collaborative Filtering
Content-Based Filtering
Random Selection
Knowledge-Based Filtering
What is the Cold Start problem in recommender systems
When the server takes too long to start.
The challenge of recommending new items or to new users with no prior data.
Lack of electricity for the system.
None of the above.
Which type of recommender system relies on the attributes of items?
Collaborative Filtering
Content-Based Filtering
Random Walk
Association Rules
What is the purpose of Hybrid Recommender Systems?
To combine two or more recommendation methods to improve accuracy.
To recommend products with the highest price.
To only use collaborative filtering.
To generate random recommendations.
What are User-Based Collaborative Filtering systems focused on?
Finding items similar to the ones a user liked.
Finding users with similar preferences to make recommendations.
Matching product features to user preferences.
None of the above.
Which metric is used to measure the accuracy of a recommender system?
Precision
Recall
Mean Absolute Error (MAE)
All of the above
Which of the following best describes Serendipity in recommender systems?
Providing expected recommendations
Providing surprising yet enjoyable recommendations
Providing the most expensive items
Providing irrelevant recommendations
What is the main disadvantage of a Content-Based Recommender System?
The system needs a large amount of user interaction data.
It can lead to a narrow range of recommendations (filter bubble).
It is unable to recommend new items.
It is always computationally slow.
Which of the following is an advantage of Knowledge-Based Recommender Systems?
They do not suffer from cold start problems.
They always require ratings.
They depend on collaborative interactions.
They use probabilistic models.
Which of the following is the cosine similarity between vectors A = [1, 0, 1] and B = [0, 1, 1]?
0.5
0.71
0.82
0.97
Which of the following is true about Adjusted Cosine Similarity?
It is used only in knowledge-based systems.
It is used to calculate diversity.
It accounts for user biases by subtracting the average rating of each user.
It assumes all users have similar preferences.
What type of similarity metric is commonly used in Item-Based Collaborative Filtering?
Pearson Correlation
Manhattan Distance
Euclidean Distance
Cosine Similarity
Which technique can be used to reduce dimensionality in user-item matrices?
Gradient Descent
Singular Value Decomposition (SVD)
Regression Analysis
Decision Trees
In which scenario is Content-Based Filtering more suitable than Collaborative Filtering?
When there is a lot of user interaction data.
When recommending new items with detailed features.
When user preferences are unknown.
When calculating similarities between users.
