You watch the stuff that sounds interesting and maybe don’t watch the stuff that doesn’t? You can base this off trailers, reviews, or just listen to people discussing movies and what they say about them, and if they make it sound like something you want to watch. The same goes for games. I have never in my life cared about a number score or a tomato meter percentage. I have never thought a movie “should” get a different number assigned to them and gotten angry over it. Who cares? That number isn’t actually part of the movie and doesn’t impact the quality of the movie. It’s just some number other people came up with that doesn’t in any way determine whether or not you will like a movie.
I’ve found when I watch low rated movies that they are typically worthy of their low rating.
Sometimes I do find that high rated games / movies aren’t as great for me (BOTW is one example) as the majority sees them.
One example is the zombie genre.
I watch a ton of them and I have yet to come across one where the aggregate review score is much different than what I felt about the film / show.
For sure I disagree with the hatred / burn out that people had with TWD as I iked it to the end.
In order to get a good prediction of “should watch / shouldn’t watch”, the system used scores on a 0-10 scale for the amount of 20+ categories present in each film… then each user would give their category preferences on a -5…+5 scale… and the sum of a film’s category scores × user preferences, would end up being highly correlated to the user’s like/dislike of the film.
From the end user’s perspective, it only required entering 20+ preferences… but scoring each film on 20+ categories, proved much more difficult. People would give different scores for their perceived amount of a category in a film, and while the personal sum[score×preference] was highly correlated to their like/dislike verdict, the sum[avg(scores)×preference] was all over the place, and we weren’t able to find a way to assign film category scores that would work reasonably well for everyone.
Turns out people not only have different category preferences, but also different category perceptions for the same film.
Maybe revisiting the idea today, with the help of some AI, could find some effective grouping or a different predictor, but back then we just mothballed the whole thing.
People give way to much importance to numbers on the internet.
They are only better than rumors and singular reviews in print.
I only have so much time.
What method do you suggest to weed through entertainment that is not worth your time?
You watch the stuff that sounds interesting and maybe don’t watch the stuff that doesn’t? You can base this off trailers, reviews, or just listen to people discussing movies and what they say about them, and if they make it sound like something you want to watch. The same goes for games. I have never in my life cared about a number score or a tomato meter percentage. I have never thought a movie “should” get a different number assigned to them and gotten angry over it. Who cares? That number isn’t actually part of the movie and doesn’t impact the quality of the movie. It’s just some number other people came up with that doesn’t in any way determine whether or not you will like a movie.
I’ve found when I watch low rated movies that they are typically worthy of their low rating.
Sometimes I do find that high rated games / movies aren’t as great for me (BOTW is one example) as the majority sees them.
One example is the zombie genre.
I watch a ton of them and I have yet to come across one where the aggregate review score is much different than what I felt about the film / show.
For sure I disagree with the hatred / burn out that people had with TWD as I iked it to the end.
I tried that (many) years ago.
In order to get a good prediction of “should watch / shouldn’t watch”, the system used scores on a 0-10 scale for the amount of 20+ categories present in each film… then each user would give their category preferences on a -5…+5 scale… and the sum of a film’s category scores × user preferences, would end up being highly correlated to the user’s like/dislike of the film.
From the end user’s perspective, it only required entering 20+ preferences… but scoring each film on 20+ categories, proved much more difficult. People would give different scores for their perceived amount of a category in a film, and while the personal sum[score×preference] was highly correlated to their like/dislike verdict, the sum[avg(scores)×preference] was all over the place, and we weren’t able to find a way to assign film category scores that would work reasonably well for everyone.
Turns out people not only have different category preferences, but also different category perceptions for the same film.
Maybe revisiting the idea today, with the help of some AI, could find some effective grouping or a different predictor, but back then we just mothballed the whole thing.