Amazon Interview Question for Software Development Managers


Country: United States
Interview Type: In-Person




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What is the carousal system on Amazon? You mean the marquee in a div that says you may like these other products??

For each user, have a list of recently bought products
For each product store a list of products bought along with it (use a heap if you want for this)
Combine these two lists together, plus a few more products based on the category of the item and items recently looked at by the user etc

- confused_coder August 21, 2016 | Flag Reply
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There can be lot of way we can recommend a Product to a particular user if we had to categorize it can be like
1. Depending upon what he is searching on
2. Relating that searched Item to what he Purchased in the history
3. Respecting User preference on Public Review of the Product
4. Products that People who bought the same product along with the product searched by the current User.
5. Best Brand Products that matches the searched product.
6. Sort according to the best discount on the product searched
7. Product which offers Free Shipping
ect

Coming to the Design

The search itself can arrange the matched items in a tree\Graph structure and as the user traversing down a path Add new product which patch the Criteria and have some kind of a circular loop if the user is exploring more and more since the system should never be able to say that it can no longer search your product.

- naween0423 September 22, 2016 | Flag Reply
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This is a typical machine learning problem statement. What can be done here is to create a matrix of all products that people bought and levering purchasing history (products they bought together with the first product). The first thing that needs to be done is to create a collaborative filter and finally do a featurized matrix factorization. The parameters of matrix factorization is a set of features for every user (gender, age, location and etc.). Through this you can predict the recommended product for the user and present it to the user. From an implementation point of view, I would believe that if the system has enough capacity, one could create a relational database of all the purchases and create the matrix factorization. Part of this database could be used as training data for the prediction model. As this system doesn't need to be a real time system, not much of requirement for the hardware and architecture is needed.

- Ferri Tafreshi January 20, 2017 | Flag Reply
Comment hidden because of low score. Click to expand.
0
of 0 vote

This is a typical machine learning problem statement. What can be done here is to create a matrix of all products that people bought and levering purchasing history (products they bought together with the first product). The first thing that needs to be done is to create a collaborative filter and finally do a featurized matrix factorization. The parameters of matrix factorization is a set of features for every user (gender, age, location and etc.). Through this you can predict the recommended product for the user and present it to the user. From an implementation point of view, I would believe that if the system has enough capacity, one could create a relational database of all the purchases and create the matrix factorization. Part of this database could be used as training data for the prediction model. As this system doesn't need to be a real time system, not much of requirement for the hardware and architecture is needed.

- Ferri Tafreshi January 20, 2017 | Flag Reply


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