Would allow discovery of new good content that hasn't employed growth hacks and will also differentiate between equally rated content.
https://aeolipyle.co (algorithm complete -- need to find good use for it.)
In terms of algorithm it's got around issues of merging incomplete Condorcet elections (as not everyone will compare or rank every item) and clustering.
Turning these partial elections into a single order ranking.
Essentially, you do binary search-insertion into a list, where the comparison function is a prompt to the user asking "Is A better than B?" (If it's too difficult to judge "betterness" between two items, you could just as easily swap in a different comparison. "Is A funnier than B?")
One thing that people always ask when I mention this is: "What if A and B are equal?" Well, then you answer no, because A is not better than B. If your answers are consistent, then A and B will end up next to each other in the list.
What you're describing technically would work when each person compares every item (and would fall into the domain of condorcet methods).
However in practice the election becomes a graph (rather than list or x/y table) with cyclical dependencies and conflicting comparisons -- it becomes quite hard to resolve -- but it can be.
Cyclical/conflicting comparisons are a function of faulty users, the algorithm can't take the blame for that! ;)