Maximize Likes from Trends
Problem Description
You are a TT content creator with predictions about which trends will be popular over the next n days. These trends are represented by an array prediction, where the trend that will be popular on the i-th day is given by prediction[i].
Before the trends gain popularity, you need to select a set of trends to focus on. For each trend x that you choose, you will earn x likes each time that trend appears in the prediction array.
However, to maintain diversity in your content, if you choose the x-th trend, you cannot choose the (x-1)-th or (x+1)-th trends, as they are considered too similar.
Your objective is to maximize the number of likes you can earn by carefully selecting which trends to focus on.
Since the number of likes can be large, return your answer modulo 10^9 + 7.
Example
Input:
prediction = [1, 3, 2]
Output:
4
Explanation: Choose trends 1 and 3. You will get 1 like on the first day and 3 likes on the second day. The total number of likes is 4.
Trending Video Engagement Decay
Problem Description
The task is to analyze the engagement decay of trending videos.
Some videos posted gain initial views represented as an array views of size n. The i-th video starts with an initial view count of views[i].
As time progresses, the view count of a video decays by a factor of resistance. However, a video's view count can only reduce to views[i] / resistance if views[i] is divisible by resistance.
Similarly, further reductions to (views[i] / resistance^2), (views[i] / resistance^3), and so on, can only occur if each intermediate result is divisible by resistance.
The goal is to find the number of unordered pairs (i, j) such that after some (possibly zero) decay rounds of video i, it reaches exactly the same view count as the video at index j (either immediately or after some decay steps).
Additionally, if two videos start with the same view count, they should be considered a valid pair even without any decay rounds.
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