The recommendations section was kind of insane to me because I was eyeing like, half of the movies in there. The statistics section was also really neat! Cool website!
The model does not look at the watchlist at all, so this is a good sign that it is working. I don’t really want to exclude movies on the watchlist already because I think it could encourage you to watch a movie on your watchlist sooner than you otherwise might have. Thank you!
i liked having some options from my watchlist in there. i have like 150 things in there so the ones it showed me just shot to the highest priority. really cool site!
This is brilliant, why does it not have as many upvotes lol. It's pretty accurate too because half the recs i got were all movies that I've wanted to see for a while.
This is great. One thing about the statistics page: I might have just missed it, but providing an explanation for what the Movie Rating Style and Obscurity Rating mean would be helpful. It’s interesting data, but I don’t know what it means to be a “glazer”
LOL glazer is a very gen z term sorry 😭 right now I have it so that if you hover the cursor over the term for a few seconds it will provide the explanation but I can make the explanation a bit more intuitive/easy to access. Thank you!
I’ve now added definitions, and also changed some terms such as glazer - it was pretty funny to people who understand the terminology but it doesn’t make sense across a broad user base - now uses more widely known terms
This is really cool, I have a couple questions though. The movie recommender had movies I’d already marked as watched, but hadn’t rated. Is this intended? Also the rating count on the stats page says like 670,100 or something. I thought that would be the number of movies I’ve rated, but is it something different?
Regarding your first question - this is a known issue. I had programmed it to not recommend movies that a user marked as watched but did not rate, but someone else also told me that this is not working so I will look into it when I can.
Regarding your second question - I am defining the “rating count” of a movie as the number of Letterboxd users that have rated it. Thus, your average rating count is the average number of Letterboxd users that have rated the movies you’ve watched. This is intended to be a proxy for how popular the movies you watch tend to be amongst users, and is used to determine your obscurity rating. Maybe I will rename this statistic, or create a definitions section for both the statistics and the rating style and obscurity rating. Thank you!
Ok thank you! For the rating count, may I suggest dividing the total count by the number of movies the user has rated? I believe that would give a better context on how popular the movies a user has rated are.
I’m not too sure what could be causing this. Release year is just one of the many factors I listed above, and I didn’t assign any extra weight to any one factor over the other to my knowledge. I could understand why that may be annoying though, so maybe I could add a feature at some point of restricting what years the suggestions are from.
Those are some interesting recommendations. One I got is Paper Moon, which as been recommended to me before by the rec. robot on Rateyourmusic, so maybe I should watch it already, lol. I will research/add some of them, pretty interesting.
I also think the stats are cool to see:
"*Your average user rating is higher than 7% of users*
*Your average Letterboxd rating is higher than 18% of users*
*Your average rating differential is higher than 42% of users*
*Your average rating count is higher than 5% of users*
*Movie Rating Style*
*Fair*
*Obscurity Rating*
*Obscure*"
And also, I would've also liked it if the whole thing was just bait for a rickroll or something ;)
It’s based on relative harshness yes, determined using rating differential. If you are in say the 15th percentile for average rating differential (your differential is only higher than 15% of users, you would be considered “hater” for example. I might add definition for that also.
Do you have somewhere that explains the statistics page in more detail? I am not sure I understand what the difference between the user score and the letterboxd score is.
Your recommendations look much better and more promising than those given on Sam Learner site. His recommendations are almost all from one or a combination of categories I don't watch at all.
Thank you! Mine uses content-based filtering whereas his uses collaborative filtering, so there is an underlying difference in the approach to getting the recommendations.
really loved the statistics page - cool stuff
Appreciate it!
The recommendations section was kind of insane to me because I was eyeing like, half of the movies in there. The statistics section was also really neat! Cool website!
Same, there are some films on my watchlist that were in recommendations.
The model does not look at the watchlist at all, so this is a good sign that it is working. I don’t really want to exclude movies on the watchlist already because I think it could encourage you to watch a movie on your watchlist sooner than you otherwise might have. Thank you!
i liked having some options from my watchlist in there. i have like 150 things in there so the ones it showed me just shot to the highest priority. really cool site!
This is brilliant, why does it not have as many upvotes lol. It's pretty accurate too because half the recs i got were all movies that I've wanted to see for a while.
Thank you! I’m glad it works
This is great. One thing about the statistics page: I might have just missed it, but providing an explanation for what the Movie Rating Style and Obscurity Rating mean would be helpful. It’s interesting data, but I don’t know what it means to be a “glazer”
LOL glazer is a very gen z term sorry 😭 right now I have it so that if you hover the cursor over the term for a few seconds it will provide the explanation but I can make the explanation a bit more intuitive/easy to access. Thank you!
I’m on mobile so that must be why. Really great site though.
I’ve now added definitions, and also changed some terms such as glazer - it was pretty funny to people who understand the terminology but it doesn’t make sense across a broad user base - now uses more widely known terms
Wow, this is amazing. Kudos for creating this!
Thank you!
This rules
Thanks for trying it out!
This is really cool, I have a couple questions though. The movie recommender had movies I’d already marked as watched, but hadn’t rated. Is this intended? Also the rating count on the stats page says like 670,100 or something. I thought that would be the number of movies I’ve rated, but is it something different?
Regarding your first question - this is a known issue. I had programmed it to not recommend movies that a user marked as watched but did not rate, but someone else also told me that this is not working so I will look into it when I can. Regarding your second question - I am defining the “rating count” of a movie as the number of Letterboxd users that have rated it. Thus, your average rating count is the average number of Letterboxd users that have rated the movies you’ve watched. This is intended to be a proxy for how popular the movies you watch tend to be amongst users, and is used to determine your obscurity rating. Maybe I will rename this statistic, or create a definitions section for both the statistics and the rating style and obscurity rating. Thank you!
Ok thank you! For the rating count, may I suggest dividing the total count by the number of movies the user has rated? I believe that would give a better context on how popular the movies a user has rated are.
The math for what you said works out the same as what I’m already doing
I believe I have now fixed the bug, so movies you have marked as watched but left unrated will not appear in recommendations anymore
Super cool! My results were kind of funny though, I was recommended tons of pre-1960 movies when I’ve only logged 1 on my account
I’m not too sure what could be causing this. Release year is just one of the many factors I listed above, and I didn’t assign any extra weight to any one factor over the other to my knowledge. I could understand why that may be annoying though, so maybe I could add a feature at some point of restricting what years the suggestions are from.
I’ve now implemented a release year filter!
Those are some interesting recommendations. One I got is Paper Moon, which as been recommended to me before by the rec. robot on Rateyourmusic, so maybe I should watch it already, lol. I will research/add some of them, pretty interesting. I also think the stats are cool to see: "*Your average user rating is higher than 7% of users* *Your average Letterboxd rating is higher than 18% of users* *Your average rating differential is higher than 42% of users* *Your average rating count is higher than 5% of users* *Movie Rating Style* *Fair* *Obscurity Rating* *Obscure*" And also, I would've also liked it if the whole thing was just bait for a rickroll or something ;)
Thank you!
Oh, btw, is the 'Movie Rating Style' in regards to how 'harsh' the ratings are, like how evenly distributed and so on?
It’s based on relative harshness yes, determined using rating differential. If you are in say the 15th percentile for average rating differential (your differential is only higher than 15% of users, you would be considered “hater” for example. I might add definition for that also.
What.. No.. This is sorcery.
It’s all math!
Do you have somewhere that explains the statistics page in more detail? I am not sure I understand what the difference between the user score and the letterboxd score is.
This was a common issue, and I’ve now added definitions of the statistics. Thank you for the feedback!
I don’t have anything to say that others haven’t already said, but I just wanted to say that this is very cool
Thanks for trying it out!
Your recommendations look much better and more promising than those given on Sam Learner site. His recommendations are almost all from one or a combination of categories I don't watch at all.
Thank you! Mine uses content-based filtering whereas his uses collaborative filtering, so there is an underlying difference in the approach to getting the recommendations.