I have certain people I categorize as “well-aligned movie watchers” like my brother that I grew up with. We have similar tastes. I find that gets me further than aggregate rating systems.
For some reason, that statement has stuck with me for a while. I finally realized why it doesn’t sit well with me. I agree with your message to some extent, but here are some problems I see with it:
1) Your brother still needs a way to find good movies (you can’t pull each other up by your bootstraps)
2) I wonder if you are thinking of movies as a binary “like” or “don’t like”. If your bother’s recommendations provide you a system for only watching movies that you end up liking, that’s a valuable resource. But I see movies as more boundless in how good they can be. Sure, there are movies that I “liked”, for example, Greyhound. I enjoyed it and didn’t have any problems with it. But it didn’t have a big impact on me and wasn’t particularly memorable. I would give that movie a Thumbs Up. Then there are movies like Lord of the Rings, 12 Angry Men, and Memento. These are movies that are very meaningful or amazing to me. I want to watch them many more times throughout my life. I would give them each a Thumbs Up as well, but that doesn’t really do it justice. I could give them a 10⁄10, but that doesn’t quite fit either, since I assume that I’ll eventually find movies I like even more than those ones[1]. So for me, finding someone who has similar preferences to me isn’t enough. I need something that can sort through the hundreds of thousands of movies out there and point me to the ones that I’ll like the most of all of them. If movies are more of a casual thing to you and you’re not trying to optimize your experience, the “like”, “don’t like” system makes sense. Otherwise, I’d like to hear your thoughts so I can further optimize my system (currently, I’m using a spreadsheet that combines data from multiple online sources).
Of course, if your statement “I find that gets me further than aggregate rating systems” really is true, then what I said here doesn’t matter.
In this way, I like IMDb more than Rotten Tomatoes, since Rotten Tomatoes has 500 movies with a 100% rating, while IMDb has only seven with a 9.0+ rating and none with a 10.0 rating, meaning there’s still room to grow. On a similar note, they have to give Oscars to somebody, so that evidence doesn’t count for as much. If the police had to arrest somebody, they might end up arresting some random homeless person just because he was the most suspicious person they could find. If the Academy chose not to give out Oscars some years, (and some years gave out multiple), it would theoretically increase the trustworthiness of the award.
That’s fair. I guess if you will allow me to re-state my idea with your ideas in mind:
Aggregate rating systems are best. But occasionally they are wrong. Sometimes I will see a movie has a high rotten tomatoes score but I still hated it. I don’t watch a lot of movies so this happens often actually. Having someone similar to me, who is watching highly rated movies can usually save me time by predicting whether I will like something or not.
You are right aggregate is best. But I think having an aligned friend who knows your preferences can help build on top of that. Or rather help me filter.
I’m my original post I failed to realize that my brother and I are both watching movies that are highly rated. I think using both in conjunction works great. So I’m not disagreeing with you, rather building off your thoughts.
But to pose an interesting question. If tomorrow, rotten tomatoes had an option to find a user who EXACTLY matched the ratings you gave. For instance both of you gave Titanic a 7, Forest Gump a 8, etc. Then the algorithm tells you this user that most matches you, watched Shrek 6 yesterday and gave it a 10⁄10. Would you prefer that algorithms suggestion over just an aggregate average rating?
There are a lot of cool algorithms that could be applied to this, even a neural net that could take your past ratings and “predict” movies you would like next. I’m sure one might be more accurate than averaging method. SVN or KNN algorithms seem promising off the top of my head.
For some reason, that statement has stuck with me for a while. I finally realized why it doesn’t sit well with me. I agree with your message to some extent, but here are some problems I see with it:
1) Your brother still needs a way to find good movies (you can’t pull each other up by your bootstraps)
2) I wonder if you are thinking of movies as a binary “like” or “don’t like”. If your bother’s recommendations provide you a system for only watching movies that you end up liking, that’s a valuable resource. But I see movies as more boundless in how good they can be. Sure, there are movies that I “liked”, for example, Greyhound. I enjoyed it and didn’t have any problems with it. But it didn’t have a big impact on me and wasn’t particularly memorable. I would give that movie a Thumbs Up. Then there are movies like Lord of the Rings, 12 Angry Men, and Memento. These are movies that are very meaningful or amazing to me. I want to watch them many more times throughout my life. I would give them each a Thumbs Up as well, but that doesn’t really do it justice. I could give them a 10⁄10, but that doesn’t quite fit either, since I assume that I’ll eventually find movies I like even more than those ones[1]. So for me, finding someone who has similar preferences to me isn’t enough. I need something that can sort through the hundreds of thousands of movies out there and point me to the ones that I’ll like the most of all of them. If movies are more of a casual thing to you and you’re not trying to optimize your experience, the “like”, “don’t like” system makes sense. Otherwise, I’d like to hear your thoughts so I can further optimize my system (currently, I’m using a spreadsheet that combines data from multiple online sources).
Of course, if your statement “I find that gets me further than aggregate rating systems” really is true, then what I said here doesn’t matter.
In this way, I like IMDb more than Rotten Tomatoes, since Rotten Tomatoes has 500 movies with a 100% rating, while IMDb has only seven with a 9.0+ rating and none with a 10.0 rating, meaning there’s still room to grow. On a similar note, they have to give Oscars to somebody, so that evidence doesn’t count for as much. If the police had to arrest somebody, they might end up arresting some random homeless person just because he was the most suspicious person they could find. If the Academy chose not to give out Oscars some years, (and some years gave out multiple), it would theoretically increase the trustworthiness of the award.
That’s fair. I guess if you will allow me to re-state my idea with your ideas in mind:
Aggregate rating systems are best. But occasionally they are wrong. Sometimes I will see a movie has a high rotten tomatoes score but I still hated it. I don’t watch a lot of movies so this happens often actually. Having someone similar to me, who is watching highly rated movies can usually save me time by predicting whether I will like something or not.
You are right aggregate is best. But I think having an aligned friend who knows your preferences can help build on top of that. Or rather help me filter.
I’m my original post I failed to realize that my brother and I are both watching movies that are highly rated. I think using both in conjunction works great. So I’m not disagreeing with you, rather building off your thoughts.
But to pose an interesting question. If tomorrow, rotten tomatoes had an option to find a user who EXACTLY matched the ratings you gave. For instance both of you gave Titanic a 7, Forest Gump a 8, etc. Then the algorithm tells you this user that most matches you, watched Shrek 6 yesterday and gave it a 10⁄10. Would you prefer that algorithms suggestion over just an aggregate average rating?
There are a lot of cool algorithms that could be applied to this, even a neural net that could take your past ratings and “predict” movies you would like next. I’m sure one might be more accurate than averaging method. SVN or KNN algorithms seem promising off the top of my head.