Here’s the scenario: I have a list of stock tickers, and users can vote a ticker up or down. Initially, I used a simple calculation where the percentage was derived from the individual vote count divided by the total votes. However, I realized that as the number of votes increases over time, each individual vote’s impact may become less significant.
To mitigate this dilution issue, I’ve considered implementing a weighted voting system. One approach I’m exploring is assigning higher weights to more recent votes, giving more importance to recent opinions rather than older ones. For example, I thought of doubling the weight of votes received within the last 7 days.
However, I’m concerned that even with this approach, the weighting may still become diluted as the overall vote count increases. I want to ensure that the calculation remains fair and accurately represents the voting trends over time.
I would greatly appreciate any insights or suggestions on how to address this issue effectively. Are there any alternative approaches or more sophisticated methods that could be employed to handle the weight calculation? Perhaps a decaying weight scheme or other techniques that take into account both recency and the overall vote count?
Thank you in advance.