Recently, I realized that text-content products are very ineffective
in content
recommendation compared to video-content products like
TikTok. I have been
thinking about how to improve text-content
products in general.
The ecosystem of text-content is viral with lots of creators and
consumers.
However, somehow, I feel like the text-content product
is not well done so far.
One thing I found is that text-content products' recommendation
engines are
very ineffective compared to video-content products'.
One important reason is
because users read a short thumbnail of
titles before deciding they want to go
inside and read.
But this boils down the entire content down to a few bytes of
characters, which
oftentimes is too low bandwidth to be effective.
So the entire success versus
failure of textual content rests on
if the title is appealing to users.
In contrast, video-content products like TikTok are much more
effective; the
first five seconds of a video provide very high
bandwidth, allowing users to
quickly and effectively decide if
they want to continue watching the whole thing.
It turns out that how the interface is designed determines if a
recommendation
engine would work or not. For text-content
products, because users can only
decide to read based on such
limited bandwidth, oftentimes recommendation
engines just makes a small amount of articles go viral .
As a result, most articles are not explored at all, preventing the
recommendation
engine from effectively discovering whether majority of the content is good or bad.