I want to maximize performance for social media posts by optimizing the time when they are published.
X: publishing_datetime, post_attribute_1, ..., post_attribute_n y: performance
X: post_attribute_1, ..., post_attribute_n y: publishing_datetime
The desired model should predict the optimal publishing_datetime for maximizing performance. Once the data can be modeled like this, the problem is solved with a regression neural network.
What I've tried:
Filtering the posts with above-average performance and using their attributes and publishing_datetime to form my desired model.
This is not ideal as a lot of data is unused and posts with particularly great performance influence just as much as posts with barely above-average performance.
Any suggestion on how to achieve this model transition?
All ideas and alternative approaches are very welcome. Thanks in advance!