Feature #3: Identify Peak Interaction Times
Explore how to identify three continuous, non-overlapping time intervals with the highest user interactions on Twitter for business accounts. Learn to implement an efficient algorithm that uses sliding window sums and dynamic programming to analyze hourly interaction data and select the maximum combined intervals. Understand the step-by-step process of calculating sums, and using left and right arrays to determine the lexicographically smallest indices for peak interaction times.
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Description
For this next Twitter feature, the company has decided to create an API that can be used by business accounts on Twitter. This API will identify three disjoint time intervals in which the most users followed or interacted with the businesses’ Tweets. You will be given the historical data of user interaction per hour for a particular business’ Twitter account. The API will have another parameter for hours. Your goal is to find three continuous intervals of a size equal to hours such that the sum of all the entries is the greatest. These time intervals should not overlap with each other.
Consider a Twitter profile with the following history of user interactions: {0, 2, 1, 3, 1, 7, 11, 5, 5} and hours = 2. The interaction array represents that this particular business’ account received no interactions during the first hour, 2 in the second hour, and so ...