Wenyin, as a product, had indeed fallen short of Ye Ge's expectations.
When all was said and done, the core issue was that the content recommendations simply weren't good enough.
They weren't even close to fully matching a user's specific tastes, let alone hitting the mark on the majority of the recommended content.
When browsing through Wenyin, you might occasionally stumble upon a few videos you liked, but you rarely encountered videos that you truly loved.
There was a distinct difference between liking and loving. A video you merely liked would only prompt the thought that it was quite good.
But a video you "loved" was one you would forward to others, urging them to watch it along with you.
Of course, if the app could consistently maintain a standard of being merely "good," that would still be acceptable.
Yet as one kept scrolling, a sense of boredom would inevitably creep in.
In the beginning, an application like Wenyin could indeed lock onto a user's specific preferences.
However, as the categories of interest expanded, the variety of videos recommended by the software grew correspondingly broader.
At that point, whether the system could still accurately deliver the exact videos a user wanted to watch became a matter of uncertainty.
At least on this application, it had failed to achieve the effect Ye Ge desired.
Ye Ge believed the primary problem went far beyond mere big data algorithms.
An algorithmic model could only provide the most basic foundation; finer adjustme…
Chapter 805