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DSCI 4520 EXAM 3 QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2025/2026

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DSCI 4520 EXAM 3 QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2025/2026 Assume that for a user-based collaborative recommender system the similarity scores are being calculated for five customers who have rated five brands of tea products: Ratings of which two customers are better to be used for predicting Customer 5's rating of the Celestial Tea products? - Customers 2 and 3 - Customer 4 and 1 - Customer 3 and 1 - Customer 2 and 1 - Answers Customers 2 and 3 Which of the followings is NOT an application of the market basket analysis? -Predicting customers spending amount -Bundling of products and services -Product placement in retail store establishments - Recommendation to customers - Answers Predicting customers spending amount In association rule extraction, lift measures what percentage of baskets that contained the left-hand product also contained the right-hand products. T/F - Answers False What similarity function we used in this course to quantify user similarities in the User-Based Collaborative Filtering model? -Pearson Correlation Coefficient Function -Euclidean Distance Function -Manhattan Distance Function -Absolute Similarity Function - Answers Pearson Correlation Coefficient Function In market basket analysis, support refers to the percentage of baskets where the rule is true (i.e. both left and right-side products are in the basket). T/F - Answers True Which statement is correct about support, confidence, and lift in basket market analysis? - Rules with higher confidence values are more effective and desirable -Support can be between -1 and 1 -Rules with lower lift values are more effective and desirable -Confidence can be between -1 and 1 - Answers Rules with higher confidence values are m

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DSCI 4520 EXAM 3 QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2025/2026

Assume that for a user-based collaborative recommender system the similarity scores are
being calculated for five customers who have rated five brands of tea products:



Ratings of which two customers are better to be used for predicting Customer 5's rating of the
Celestial Tea products?




- Customers 2 and 3

- Customer 4 and 1

- Customer 3 and 1

- Customer 2 and 1 - Answers Customers 2 and 3

Which of the followings is NOT an application of the market basket analysis?



-Predicting customers spending amount

-Bundling of products and services

-Product placement in retail store establishments

- Recommendation to customers - Answers Predicting customers spending amount

In association rule extraction, lift measures what percentage of baskets that contained the left-
hand product also contained the right-hand products. T/F - Answers False

What similarity function we used in this course to quantify user similarities in the User-Based
Collaborative Filtering model?



-Pearson Correlation Coefficient Function

-Euclidean Distance Function

-Manhattan Distance Function

-Absolute Similarity Function - Answers Pearson Correlation Coefficient Function

, In market basket analysis, support refers to the percentage of baskets where the rule is true (i.e.
both left and right-side products are in the basket). T/F - Answers True

Which statement is correct about support, confidence, and lift in basket market analysis?



- Rules with higher confidence values are more effective and desirable

-Support can be between -1 and 1

-Rules with lower lift values are more effective and desirable

-Confidence can be between -1 and 1 - Answers Rules with higher confidence values are more
effective and desirable

In the practice of association rules extraction, we can add virtual items to the transactional data
set such as customer age, number of children, and income. T/F - Answers True

Which statement is INCORRECT about association rule extraction methods?



-Association rule extraction requires transactional data

-Association rules extraction is heavily used in marketing

-Association rules imply causal relationship

-Association rule' structure is: IF A THEN B - Answers Association rules imply causal
relationship

Which of the following statements is NOT an issue of the user-based collaborative filtering
system?



- Interpretability of results - With a large set of items, it becomes more difficult to interpret the
outcome of the model

- Scalability - Computations become slower as the number of users and items increase

- Sparsity of records - With a large set of items, users will likely only have rated a few items

- Popularity bias - cannot recommend items to someone with unique tastes - Answers
Interpretability of results - With a large set of items, it becomes more difficult to interpret the
outcome of the model

In the user-based collaborative filtering method, the Pearson correlation similarity function is

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