This is interesting, especially considering that it favors low-data items, as opposed to both the confidence-interval-lower-bound and the notability adjustment factor, which penalize low-data items.
You can try to optimize it in an explore-vs-exploit framework, but there would be a lot of modeling parameters, and additional kinds of data will need to be considered. Specifically, a measure of how many of those who viewed the item bothered to vote at all. Some comments will not get any votes simply because they are not that interesting; so if you keep placing them on top hoping to learn more about them, you’ll end up with very few total votes because you show people things they don’t care about.
Yep. You’d want to check or guess the size of the user’s monitor and where they were scrolling to, and calculate upvotes-per-actual-user-read. As things are read and not upvoted, your confidence that they’re not super-high-value items increases and the value of information from showing them again diminishes.
This is interesting, especially considering that it favors low-data items, as opposed to both the confidence-interval-lower-bound and the notability adjustment factor, which penalize low-data items.
You can try to optimize it in an explore-vs-exploit framework, but there would be a lot of modeling parameters, and additional kinds of data will need to be considered. Specifically, a measure of how many of those who viewed the item bothered to vote at all. Some comments will not get any votes simply because they are not that interesting; so if you keep placing them on top hoping to learn more about them, you’ll end up with very few total votes because you show people things they don’t care about.
Yep. You’d want to check or guess the size of the user’s monitor and where they were scrolling to, and calculate upvotes-per-actual-user-read. As things are read and not upvoted, your confidence that they’re not super-high-value items increases and the value of information from showing them again diminishes.