Recently, our CSOAHELP team helped a client successfully pass a real technical interview at Amazon. Today, we want to share this success story: a recommendation system problem that seemed simple at first glance but actually tested engineering thinking and logical expression skills at a deep level. Fortunately, with the help of our remote real-time interview assistance service, the candidate performed steadily at critical moments and ultimately passed the interview.
During this Amazon interview, the interviewer presented the following problem:
Problem: The feature recommends a set of products that you have not already purchased from the things that your friends have bought, in order of most bought to least bought.
Complete the function that takes in a person and returns the recommended products.
To help with this, we have the following 2 APIs that you can use in your solution:
Returns a list of products purchased by the person. If purchased multiple times, it will appear multiple times in the list. Returns a list of friends of the person.
When the problem was introduced, the candidate immediately felt the pressure. On the surface, it looked like a simple set operation, but in reality, it required handling deduplication, counting occurrences, sorting, and careful edge case management, such as friends who bought the same product multiple times or an empty friends list. Normally, a candidate who was not fully prepared would easily stumble or miss key details during thinking and expression.
As soon as the interview officially began, the candidate received our real-time textual guidance on screen: first, fetch the candidate's own purchase list and store it in a set for quick lookup; then retrieve the friends list and iterate through each friend to count the number of times each product was purchased; while counting, filter out products already purchased by the candidate; finally, sort the products by frequency of purchase to generate the recommendation list.
Following the guidance, the candidate clearly articulated the solution to the interviewer. After hearing it, the interviewer expressed approval. Moving into the coding phase, we also synchronized a detailed code framework in the background, including function structure, variable naming, and necessary boundary handling. This allowed the candidate to focus on small details while ensuring the overall logic was flawless.
Once the code was completed, the interviewer immediately launched a follow-up question: if the friends list is extremely large, say tens of thousands of entries, can your solution remain efficient? Faced with this question, the candidate hesitated slightly. CSOAHELP instantly pushed an analytical hint, suggesting the candidate explain the current time complexity as O(number of friends × number of products), which is acceptable for moderate data sizes. If handling huge data, distributed counting (like Map-Reduce) or batch processing of friends should be introduced to reduce frequent database access. The candidate quickly restated this answer, satisfying the interviewer.
The interviewer then raised a more advanced question: if the interests between friends differ greatly, the recommended products might be inaccurate. CSOAHELP immediately pushed business-level thinking directions, such as introducing an interest similarity scoring mechanism to only consider friends with high overlap, setting a minimum similarity threshold to filter irrelevant friends, or adding a relevance scoring system to the recommendation list. The candidate expanded on these suggestions in detail, demonstrating solid product understanding and business modeling abilities, earning frequent nods from the interviewer.
Finally, the interviewer posed an open-ended question: if you were to implement this feature in Amazon's production environment, what system-level challenges would you encounter? We immediately pushed system design ideas, suggesting considerations like caching popular products to reduce real-time computation pressure, generating recommendation results asynchronously to ensure interface response speed, and addressing data skew where hyperactive users' purchase data might heavily bias the results. The candidate smoothly explained all these points and added mitigation strategies, ultimately impressing the interviewer with a strong display of systematic thinking.
A few days later, the candidate successfully received an Amazon offer notification.
This interview reaffirmed a critical truth: interviews at top tech companies are no longer about solving coding puzzles but have evolved into a comprehensive test of thinking skills, expression abilities, system vision, and on-the-spot adaptability. CSOAHELP's remote real-time interview assistance is designed precisely for this complex battlefield.
Throughout the interview, we observed the process silently, providing complete thought guidance at every critical juncture, accurately predicting potential follow-up questions before they were asked, and offering standardized code frameworks when necessary. This ensured that even candidates with limited real-world experience could consistently deliver big tech-level answers and code, staying calm, coherent, and successful.
CSOAHELP's remote assistance is not about answering for you but about amplifying your true strengths and making sure your potential is fully perceived by the interviewer.
If you are preparing for interviews at Amazon, Google, Stripe, Apple, or other top-tier companies, and do not want to fail due to nerves, stumbles, or overlooked details, now is the time to contact CSOAHELP. Let us become the strongest support for your career leap.
经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。
If you need more interview support or interview proxy practice, feel free to contact us. We offer comprehensive interview support services to help you successfully land a job at your dream company.
