Your first point isn’t true because most of the 5 star usage boils down to 1/5 stars and not 2-4. There are some users that use whole range and the info does have value. The main problem though is while that system may be better for certain users, it will be worse for the entire user base as you want a system that works well for both casual and power users. Really you want a system that works better for casual users as most users on typical system like YouTube/spotify/tiktok/etc would be casual users. And while having model distinguish between user types is possible, ui wise you aren’t going to want to have both thumps up/down as an option and 5 stars. A toggle is possible but even for users that would use full ratings, a toggle is fairly unlikely to be used.
Even for power users it may not be net benefit for them. Recommendations are not based solely on that user’s ratings but all ratings. If power users give better signal but total quantity of signals decreases then it’s likely that recommendation quality drops for both casual/power users. Making up guess numbers a 5 star system is likely to get you 3x less data points and many of those data points are of similar quality anyway (users that only rate 1/5 stars). Worse signal that has higher volume often leads to better model.
Even for power users it may not be net benefit for them. Recommendations are not based solely on that user’s ratings but all ratings. If power users give better signal but total quantity of signals decreases then it’s likely that recommendation quality drops for both casual/power users. Making up guess numbers a 5 star system is likely to get you 3x less data points and many of those data points are of similar quality anyway (users that only rate 1/5 stars). Worse signal that has higher volume often leads to better model.