1. Explain to the client the type of data generated, collected and what it will be used for.
The data generated by the system will include products that the customer may want to purchase based on their shopping history and interests. The data collected by the system will include previous purchases and other customer interests. The data will be used to provide the customer with recommendations in order to make their shopping experience easier.
2. Explain the expected outcomes (positive and negative).
The positive outcomes of the system will be that the customers shopping experience will be enhanced on Ebay. This will help customers find products they are interested in quickly. The negative outcome is that the data collected from customers may compromise the customer’s privacy.3. Explain the process which you will use to analyze the data at your disposal.
Since the website has a vast amount of data is available, data mining techniques will be used to analyze the data. This will involve data preparation, data modelling, and deployment.
On the business side:
1. Meeting business requirements for a recommendation system should be your priority when deciding on which data should be generated. Explain how the system requirements led your decision making process for generating data. Since the system requirement is to tailor recommendation to the customer, their previous purchases, auctions and other relevant information, my decision involves collecting data on previous customer purchases and other customer interests. This data will then be analyzed using data mining techniques in order to provide the most appropriate recommendation to the user thus meeting the system requirements.
2. Generate a plan for what happens in the cases that a customer clicks on the recommendations after a certain time. In the case that a customer clicks on a recommendation after a certain time, the recommendation should open in a new tab on the customer’s browser. This will allow the customer to view the recommendation.
Generate a plan for what happens in the cases that a customer doesn’t click the recommendations after a certain time and propose a solution to this problem. In case the customer does not click the recommendation after a certain period, a new recommendation will be generated. This is mainly because the system created will be dynamic in nature. Therefore, once a prescribed amount of time has elapsed and the customer has not clicked a recommendation a new recommendation will be generated by the system.
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