NPTEL E-Business Assignment Answers 2024
1. In a customer behavior model graph, the sum of the probability of going from exit state to itself and from exit state to the entry state is 1. Which physically indicates
a. The customers may or may not return to the website
b. The customers will always return to the website
c. The customers will never return to the website
d. The customers are yet to visit the website
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2. What is average session length?
a. The average number of pages accessed from a web server per month
b. The total number of links in the customer behavior model graph divided by the number of number of nodes
c. Average of all the entries in the transition probability matrix
d. Sum of the average number of transitions made from every state of a website to all other state.
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3. How do you build the average holding time matrix?
a. (ij)th entry in the matrix is created by dividing the accumulated think time between the states i and j by the total number of states
b. (ij)th entry in the matrix is the transition count between states i and j
c. (iJ)th entry in the matrix is the accumulated think time between the states i and j
d. (ij)th entry in the matrix is created by dividing the accumulated think time between the states i and j by transition count between them
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4. Which of the following performance parameter cannot be determined from a queueing model?
a. Server utilization
b. Average no of requests per second
c. Average throughput of the server
d. Server Scalability
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5. Which of the following matrix is not a part of basic recommender system framework
a. User matrix
b. Context matrix
c. Preference matrix
d. Item matrix
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6. Which of the following recommender system is not personalized?
a. Reputation based
b. Popularity based
c. Content based
d. Association based
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7. Which of the following is the right order of the tasks to be performed under content based system before actual recommendation is generated.
a. Representation, Feature extraction and selection, User profile learning
b. User profile learning, Feature extraction and selection, Representation
c. Feature extraction and selection, User profile learning, Representation
d. Feature extraction and selection, Representation, User profile learning
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8.
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9. In a movie recommender system, the short description of the movie can be used to extract____________ using text analytics.
a. Extrinsic Features
b. Intrinsic Features
c. Profile Feature
d. Content based feature
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10. The data set to be used for decision tree generation has to be__________type.
a. Discrete
b. Continuous
c. Alpha-numeric
d. All of the above
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