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Please write your answer by 1.7. Thank you every much. Q1 QUESTION 1 34 Points A lifestyle magazine has a strategy to expand its services
Please write your answer by 1.7. Thank you every much.
Q1 QUESTION 1 34 Points A lifestyle magazine has a strategy to expand its services by offering a free mobile app that provides a personalised guide for restaurants on demand. They would like to explore different personalisation features that are possible to be generated by different recommender algorithms. Q1.1 6 Points Describe a proposed personalisation feature that is based on collaborative filtering. Include in your description examples of background data and input data required, and the chosen algorithm. [max 120 words] Enter your answer here Save Answer Q1.2 5 Points For the algorithm you have suggested in Q1.1., describe two strengths and two limitations, and illustrate with examples referring to the specific application context. [max 100 words] Enter your answer here Save Answer Q1.3 6 Points Describe a proposed personalisation feature that is based on a content-based filtering. Include in your description examples of background data and input data required, and the chosen algorithm. [max 120 words] Enter your answer here Q1.4 5 Points For the algorithm you have suggested in Q1.3., describe two strengths and two limitations, and illustrate with examples referring to the specific application context. [max 100 words] Enter your answer here Save Answer Q1.5 5 Points You are asked to compare the performance of the algorithms you have suggested in Q1.1. and Q1.3., focusing on their accuracy. Suggest two metrics that you can use to compare the algorithms and justify why you have selected these metrics. [max 100 words] Enter your answer here Save Answer Q1.6 4 Points Describe how you will collect the required data to apply the metrics suggested in Q1.5. [max 80 words] Enter your answer here Save Answer Q1.7 Q1.7 3 Points Which of the following are usability challenges that can apply to the recommendation algorithms you have suggested in Q1.5? reliability of the user model diminished comprehensibility taking over control from the user cold start "Grey sheep" items diminished breadth of experience Save
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