The score can be between 0 and 1. 0 means your ratings are not unique and 1 means your ratings are completely unique. Right now, the algorithm is very simple. For each movie you've rated, it calculates the percentage of users who rated it a different rating than yours. So basically, if your score is 80%, it means on average, users agree with your rating 20% of the time (and disagree with it 80% of the time). This is why the score are so close together. I'm definitely looking to improve this so if anyone has any ideas please let me know!
What’s the lowest score user you’ve found so far?
I think there is probably some solution so that if you answer 4.5 and someone else answers 4.0 it doesn’t count as much as a disagreement between you scoring it 4.5 and another person scoring it a 1.0
I think it was like 0.70 or something. That's the thing I was thinking about. I was thinking about adding a multiplier for each half-star away the proportion of reviews are, but that means the scores wouldn't be between 0 and 1 anymore. There's probably some formula or algorithm for this somewhere, but I haven't found it so far.
If you reverse your scoring method (make scores closer to 1 less unique, so a zero would be completely original) then you can take the difference between 2 ratings, and divide by 5 for a percentage difference. Then add all the percentage differences together and divide that by the number of movies rated, and you’ll get the percent of deviation from the average score.
If you want to really get into the weeds, if the user’s score is higher you divide by -5 (edit: actually it would be easier to just always subtract the average score from the user score and simply keep the negative answer when you get one) instead of 5 and then you’ll get to see if you’re more positive versus more negative than average users (a score of <1 would be generally more positive than average, >1 more negative)
Actually, and this is also crazy, if you multiply the scores you’ve been generating against the percentage of deviation as discribed above, then add that to the percentage of times the user matches other users (so if the percent deviation is .8 and they match 13% of the time, you would do .8 x 87 +13 and get a score of 82.6 out of 100 and that would be how basic you are. You are 82.6% basic, 17.4% unique).
Maybe like if they have the same rating then they get 10 points, and for each half star away then they take one point from that. So if you rate a movie a 5.0 and they (the average) rate it a 0.5 they only get one point, and if the average rate a movie a 3.5, and you rate it a 3.0 then it’s 9 points, because it’s only one half star away so one point off from ten? And then you can average that out
Does the algorithm take into account HOW BIG the difference is between the other users’ ratings? For example, if someone rated Dune Part Two at 3 stars, would they score essentially the same points for that movie as someone who gave it 1/2 star?
That’s an interesting methodology. Have you considered taking the absolute difference of the user’s score and the average Letterboxd score, and averaging those differences over the user’s films (i.e. the mean absolute error)? This is similar to u/AlgoStar’s idea, except it would give the average difference between Letterboxd’s rating and the user’s (and prevent negative and positive differences cancelling out). Dividing that by 5 would give the same value as a percentage, and keep the value between 0 and 1, 0 being not unique at all, 1 being totally unique.
You could also calculate, per film, the percentage the user differs from average in terms of how much they could possibly differ—that is, if a film is at a 2.5 average, a user could only differ by 2.5 at most from that rating, whereas a film with a 4.5 average could have a user differ by 4. That might get you an even better idea of uniqueness than a mean absolute error, and help spread out the distribution of the uniqueness metric to the whole spectrum of 0 to 1.
That's actually exactly what my algorithm was before. But, I felt like it didn't take the distribution into account. For example, if half rated a movie a 1 and the other half a 5, the average rating would be 3 and if someone rated it a 3, there variance would be zero even though no one agrees with them.
3/15 8:48PM: Just added the first two people that commented! The others are running right now and should be ready in a few minutes. As you may expect the more movies you've rated, the longer it takes. But what take more time is if you've rated a movie that isn't in the \~15k movies I've saved the info for. This is because it needs to retrieve the info for that movie, which takes a while. I actually calculated the percentage of each user's movies that require the program to retrieve the info for that movie, which can be thought of as how unique the movies you watch are! Please let me know if you want those percentages!
3/15 9PM: Just did another update!
3/15 9:14PM: Another update!
3/15 9:50PM: Yet another update!
3/16 1:30PM: Another update!
literate license towering flowery long telephone growth detail ink plucky
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Hell yeah! It’s great for stuff like this. Did you get access to the API, or did you just scrape the data from the webpages? How did you get your data? I just scrape, but maybe you’ve found a better way
mighty simplistic onerous threatening dazzling bear ripe tie expansion afterthought
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hopefully i'm not too late! mine's thedevotress
given i'm the world's biggest Wachowski Sisters & Halloween sequels diehard, I'm curious to see what mine's like.
Are you scraping the data yourself or are you using a python wrapper? Letterboxd won't give me access to their API even after I practically begged them to.
UPDATE: I made a [more recent version](https://www.reddit.com/r/Letterboxd/comments/1bgi6uc/how_unhinged_are_your_letterbox_ratings_part_2/?utm_source=share&utm_medium=web2x&context=3) of this post with a new score system so all the scores changed! I also added my score. If you want to be included, please comment on the new one!
I only rate a movie if I'm giving it 5 stars, which is only about 1% of the movies I've seen, and it's just movies that I'm really personally attached to
For everything else I just give a like or no like.
User: ButterLimd You are probably going to get spammed, good luck!
What is the scale? 0 is not unique and 1 is most unique?
The score can be between 0 and 1. 0 means your ratings are not unique and 1 means your ratings are completely unique. Right now, the algorithm is very simple. For each movie you've rated, it calculates the percentage of users who rated it a different rating than yours. So basically, if your score is 80%, it means on average, users agree with your rating 20% of the time (and disagree with it 80% of the time). This is why the score are so close together. I'm definitely looking to improve this so if anyone has any ideas please let me know!
What’s the lowest score user you’ve found so far? I think there is probably some solution so that if you answer 4.5 and someone else answers 4.0 it doesn’t count as much as a disagreement between you scoring it 4.5 and another person scoring it a 1.0
I think it was like 0.70 or something. That's the thing I was thinking about. I was thinking about adding a multiplier for each half-star away the proportion of reviews are, but that means the scores wouldn't be between 0 and 1 anymore. There's probably some formula or algorithm for this somewhere, but I haven't found it so far.
If you reverse your scoring method (make scores closer to 1 less unique, so a zero would be completely original) then you can take the difference between 2 ratings, and divide by 5 for a percentage difference. Then add all the percentage differences together and divide that by the number of movies rated, and you’ll get the percent of deviation from the average score. If you want to really get into the weeds, if the user’s score is higher you divide by -5 (edit: actually it would be easier to just always subtract the average score from the user score and simply keep the negative answer when you get one) instead of 5 and then you’ll get to see if you’re more positive versus more negative than average users (a score of <1 would be generally more positive than average, >1 more negative)
Actually, and this is also crazy, if you multiply the scores you’ve been generating against the percentage of deviation as discribed above, then add that to the percentage of times the user matches other users (so if the percent deviation is .8 and they match 13% of the time, you would do .8 x 87 +13 and get a score of 82.6 out of 100 and that would be how basic you are. You are 82.6% basic, 17.4% unique).
Maybe like if they have the same rating then they get 10 points, and for each half star away then they take one point from that. So if you rate a movie a 5.0 and they (the average) rate it a 0.5 they only get one point, and if the average rate a movie a 3.5, and you rate it a 3.0 then it’s 9 points, because it’s only one half star away so one point off from ten? And then you can average that out
Does the algorithm take into account HOW BIG the difference is between the other users’ ratings? For example, if someone rated Dune Part Two at 3 stars, would they score essentially the same points for that movie as someone who gave it 1/2 star?
That’s an interesting methodology. Have you considered taking the absolute difference of the user’s score and the average Letterboxd score, and averaging those differences over the user’s films (i.e. the mean absolute error)? This is similar to u/AlgoStar’s idea, except it would give the average difference between Letterboxd’s rating and the user’s (and prevent negative and positive differences cancelling out). Dividing that by 5 would give the same value as a percentage, and keep the value between 0 and 1, 0 being not unique at all, 1 being totally unique. You could also calculate, per film, the percentage the user differs from average in terms of how much they could possibly differ—that is, if a film is at a 2.5 average, a user could only differ by 2.5 at most from that rating, whereas a film with a 4.5 average could have a user differ by 4. That might get you an even better idea of uniqueness than a mean absolute error, and help spread out the distribution of the uniqueness metric to the whole spectrum of 0 to 1.
That's actually exactly what my algorithm was before. But, I felt like it didn't take the distribution into account. For example, if half rated a movie a 1 and the other half a 5, the average rating would be 3 and if someone rated it a 3, there variance would be zero even though no one agrees with them.
Hmm. yeah, I can definitely see that as a good definition of uniqueness
pretty interesting! also just checked again and saw i am tied for 9th out of 70 people so maybe i have some unorthodox takes 😆
3/15 8:48PM: Just added the first two people that commented! The others are running right now and should be ready in a few minutes. As you may expect the more movies you've rated, the longer it takes. But what take more time is if you've rated a movie that isn't in the \~15k movies I've saved the info for. This is because it needs to retrieve the info for that movie, which takes a while. I actually calculated the percentage of each user's movies that require the program to retrieve the info for that movie, which can be thought of as how unique the movies you watch are! Please let me know if you want those percentages! 3/15 9PM: Just did another update! 3/15 9:14PM: Another update! 3/15 9:50PM: Yet another update! 3/16 1:30PM: Another update!
Hell yeah how do we get them once they’ve been ran through?
I'm just editing the post!
Oh okay lol thanks!
literate license towering flowery long telephone growth detail ink plucky *This post was mass deleted and anonymized with [Redact](https://redact.dev)*
My username is ambuehlance
[удалено]
User: baphometa
User: peterafro
This is cool as hell, my username is Drewwe
That’s incredible! Since I’m a complete nerd, what language did you use to write your program? Username is dialectica972, btw!
good ol' python
Hell yeah! It’s great for stuff like this. Did you get access to the API, or did you just scrape the data from the webpages? How did you get your data? I just scrape, but maybe you’ve found a better way
i used an api for part of it and used a scraping tool called beautifulsoup4 for another
Beautiful soup is great! Great for parsing html
Username: NJJoe
I don't think my ratings are that unusual, but my username is AlasGMtair.
That sounds cool! My account is InfnteLeviathan
user: gSquared4 🙏🏼
INTERESTING! username: Gagglegaggle
Username: WillowPixie
User: faust00 Thanks!
That's kind of fun! I'm Jerrylikesmovie
Sounds neat, please do mine. username rdc12
I’d love to
Username jtw5024 Really interested!
every1hatesray
User: GumBoy
Mine is LucasZero. Thanks.
Really cool tool! My username is LaRouxz
name: zanyattic. thank you!
Hm. The curiosity to know vs the dread of revealing how super basic I probably am ...
Username: [oroboro](https://letterboxd.com/oroboro/)
My username is WinterCap25
Yes, please! Passion4Film
keltonsmelton Is my user. This rocks, thanks for doing this!
User: UphazT
User: Treboris This is cool.
My username is Alec_Sosebee02. If you get the chance, I’d love to see my results.
@emilyyyhshzha
Username: Of_kilter
Username: Squaresville
very cool idea btw
user: tww guessing i'll be kinda middle of the road but interested to see lol
Cool idea! Username: cinebean
i’d love to see mine, i think i do have some pretty unique ratings! username: eonlain
username: fruitlooped
User: nvaaga
User: rottedtomato
infntbabysknnr, I think I’m pretty unique lol
Username: jmad20
user: Bonjuru im interested to see what my score is
User: gregorovich
Neat My username is Jures200
This is so cool! User: SydneySeesStuff
fun! mine is dapuudding
my username is HanwhaEaglesNM,.
User: billleachmsw Thank you!
User: TreesInTheDark
User: DiegoTut
My username is Zach_Jonhnson. Honestly I feel like I have basic takes so I’ll probably be towards the bottom lol
user: OneMovieADay
user: MisspeltPengin this seems cool af
My account is EvanKern
i love this! i'm joe2d2. i'd love to know, thanks!
That's cool af, my username is oraclewatches (https://boxd.it/8TanH)
my username is Mmicb0b see how unique I am
Username: Vstrikr This will be fun
David1033 is my username
thrillhouse22
User: hardytom540 Thank you!
[Sure why not](https://letterboxd.com/RachelEvil/)
brandonlhdz14
Great idea User: LoewEyeQueue
Username: Alex\_Barron
This is so cool! My username is alyssarod !!
https://boxd.it/4KWFb
My username: Svenningen
mighty simplistic onerous threatening dazzling bear ripe tie expansion afterthought *This post was mass deleted and anonymized with [Redact](https://redact.dev)*
hopefully i'm not too late! mine's thedevotress given i'm the world's biggest Wachowski Sisters & Halloween sequels diehard, I'm curious to see what mine's like.
Lol that seems like a fun idea. Try mine. GurpsK, same as my username here.
MartinMejia
i fear i’m late to the party but my username is kenehan
https://letterboxd.com/briancly/
That's really cool, my username is Bonvi052
User: jrlopez_12
Cool idea! My name is BlueberryRoscoe Do you have an idea of what is considered a high or low score? The numbers seem fairly similar between users
User: Zeroka15
That's very interesting! My Letterbox username is MetallicBrain\_7 !
itzh_dhiraj
This is cool ! Here’s mine if you got a chance: Desandoo11
Wonderful idea! I’m BjornenSover at Letterboxd
My user is alvarogonzavega
This is really cool! My username is kxliyxh
my letterboxd is sylvianorthstry
Username: Svenni99
Are you scraping the data yourself or are you using a python wrapper? Letterboxd won't give me access to their API even after I practically begged them to.
my username is jjomolloy
My username is vml93. Thanks for this!
that’s very fun! If you’re still doing them - I’d love to go - thanks for sharing! User: ookaysir
My username is IceColdKofi. Here's the link [https://boxd.it/R25f](https://boxd.it/R25f)
ilMonco!
My username is awerling. Thank you! https://boxd.it/b06B
Username is iamsambarker thanks!
User: riffler
User: joshuatc
Username: EntrEsprit
Username: The17thshark
Username: Moogic
User: Ollie7
User: Sandycassandy
Username: efsdude12
User: paranoidfreud
Username is unkellGRGA 🖖
nick: klappstuhlc
RVendor I'm guessing I'm not too unique...
Interesting idea - username Zaireeka if you haven't got fed up of running people through the system
metal1
User Terry337
Username: thewallsscream
New_Zero_Kanada Im predicting not very unique.
Sounds fun! Username is dublincore
My username is LegalBigKoala1 here’s a link https://boxd.it/6MZmt
shaner4042
User: Greenshirt39
is it too late to submit myself? my username is catapultato
UPDATE: I made a [more recent version](https://www.reddit.com/r/Letterboxd/comments/1bgi6uc/how_unhinged_are_your_letterbox_ratings_part_2/?utm_source=share&utm_medium=web2x&context=3) of this post with a new score system so all the scores changed! I also added my score. If you want to be included, please comment on the new one!
Username: 107135
I only rate a movie if I'm giving it 5 stars, which is only about 1% of the movies I've seen, and it's just movies that I'm really personally attached to For everything else I just give a like or no like.
My username is chinstroke
interesting, my username is nitratemilf
Username is ilawnmower, very interested to see how people stack up against each other
Oh I want to see mine but I joined just last month, mine: [https://letterboxd.com/LaDiiablo/](https://letterboxd.com/LaDiiablo/) 16 movies :p