What is Reddit's opinion of
MovieLens?
From 3.5 billion Reddit comments
31 reviews of this app found across Reddit:
Check out MovieLens. No movies to stream, just stuff to rate on one side, and recommendations out the other side. It's a research project, so none of those biases like "please watch original content." And you have choices between recommendation algos. Rate stuff on it, and it'll learn about you at least as well as netflix, with the pro/con that it can recommend things without being limited to what's available on netflix.
I did this several years ago; watched a movie a day for a year as part of a "fun" New Year's resolution instead of yet another "lose weight" one. But I'm not an artist & didn't sketch them.
I chose them from a list I'd pre-compiled of films that reviewers (Ebert was still alive then) said I'd like and from several websites that use your ratings of previous films to recommend new ones. I'd TiVo films that came on TCM and AMC and numerous other channels, rent them from Netflix, watch (and throw away) old VHS tapes I'd recorded but never gotten around to watching, etc. A few were seen in theaters and/or on planes during vacations. A wide variety of sources, which helped keep me fully-supplied with movies to watch.
O que o YouTube erra o Spotify acerta pra mim. Parece que ele me conhece melhor do que meus amigos. Agora o melhor algoritmo de recomendação que já vi é o do movielens. Acerta tudo pra mim.
It's an academic non-profit and so, unlike Flixster and IMDb, there are no ads, nor are they attempting to sell you tickets.
It's neat because it provides detailed breakdowns about how you rate films, such as which genres you tend to like, what time periods your movies tend to be from, etc.
I don't go by ratings. I extrapolate from good and bad reviews and other information (such as "Because you liked ... ") to decide if I might like the book. I'm pretty good at it, I think. But I've read enough "bad" books that got high ratings and "good" books that got low ratings, that I hardly give low ratings weight. That said, I still give high ratings some weight, as books that are highly rated are usually highly rated for a reason. For example, I've picked up books from Goodreads's Highest Rated list.
I also will look at other users with similar tastes as my own. When I start seeing the same faces popping up in the reviews sections of all the books I read, that's a good time to click on their profile. Do they agree with me on a lot of ratings? Great! Start perusing all their titles!
Now, what I don't like about Goodreads. There is no collaborative rating; that is, the engine is not capable of accurately telling me how I will rate a book based on the millions of data points from other users and I. So recommendations are also awkward a lot. I wish they had tags instead of bookshelves, although the concept is similar. A bounty of tags make it easier to tie similar books together (take a look at the great movie recommendation site, https://movielens.org/). There are many great books I've read but not entered into Goodreads that in theory should be showing up if Goodreads had an accurate recommendation engine. Maybe others have had a different experience? But I don't get many book ideas through the recommendations part of the site. Then again, I haven't read many of the recommendations, either, so how do I really know? lol
edit: Added thought about a tagging system.
movielens.org is what I use. It advertises itself as "Non-commercial, personalized movie recommendations". I like that it predicts what I'll rate a movie. It's usually within half a star of my actual rating.
Cinesift does a good job of showing all the rating services together. But of late I have found [LetterBoxd](www.letterboxd.com) do a great job. Also, personally, I use taste.io to get movie ratings that are based on my likes and dislikes. It is immensely helpful when picking an old move (<1990) to watch. Another great option is movielens where I get to have a look at similar films and their prediction of my future ratings is quite good.
It is an research project at the University of Minnesota using collaborative filtering. It's also open dataset, so I know that I am helping researchers develop new recommendation engines that don't just get locked up in some closed database somewhere.
Lastly, it's ad-free and it's easy to import ratings from IMDB and to export it from MovieLens.
Awesome - I didn't know this existed, I'm going to try it now.
I'd also recommend Movie Lens which 24hrs after signing up and rating stuff gives you absolutely fantastic recommendations via their wizard algorithm. Basically half of my watchlist is being recommended to me, even the more obscure stuff; it's spot on!
Check out movie lense. You do have to make an account, but it’s free and really comprehensive. Way better algorithm than the thumbs up/thumbs down that most streaming services use.
Not integrated into plex or anything, it's just a good recommendation engine. Once you rate about 25 movies it starts predicting ones you'll rate highly. It's surprisingly accurate I've found.
Suggests movies to watch based on your movie reviews. Unfortunately, not a whole lot of users so the algorithm can't do its magic too well.
If anyone can share alternative it would be much appreciated.
Also Movielens.org is a university based AI review system that allows you to rate what you have seen and then uses AI to suggest what else you should watch. It is very addictive and has great suggestion system :-D
Have you checked these: https://letterboxd.com/ https://movielens.org/
Apart from the tech - what is the rationale on which you are classifying movies? IMDB takes the average of ratings (and therein also has some rules, like for e.g., ignores the ratings from recent users or those who rarely vote). I have been thinking on a few ways, but still haven't found a good enough way to group movies, which could be a recommendation repository to begin with. And for that matter, I think the IMDB recommendations are not good, but most people find them ok.
This is one of the first times I’ve ever used something like this that actually gave me great bands/artists.
For those of you looking for good movie recommendations as well, I highly recommend Movie Lens
If you like this and you'd like to learn more about movie recommendation, check out https://movielens.org/ with data available at https://grouplens.org/datasets/movielens/. GroupLens is a computer science research lab at the University of Minnesota focusing on recommender systems.
Hold the fucking presses nobody has said Brick yet??? BRICK dude!! You'll fucking love it I promise. You'll never see another movie quite like it. The only movie I can recommend harder is The Fountain because it had the best soundtrack of any movie you or anybody else has mentioned so far.
Also highly recommended:
Wristcutters: A Love Story (unique character to the world and the feel of it)
Kiss Kiss Bang Bang (can you tell I like Noir yet?)
Sin City
Into The Spider-Verse (soundtrack + style, check and check)
eXistenZ
Pulp Fiction
Memento
The Matrix
In Bruges
Dark Crystal
Dark City
Enemy
You can find more here: https://movielens.org/explore?tag=stylized&sortBy=tagScore&page=7
If you rate some movies, it can even recommend movies based on your preferences with some machine learning shenanigans.
That sounds great, though I personally don't think the UIs on the streaming services I use are that bad. Then again, I use third party services like Movie Lens, Rotten Tomatoes, and Just Watch to help me find the content I want to watch, so by the time I get to the app, I usally just type in what I want and hit play.
Try https://movielens.org. The site is run by a research lab at the University of Minnesota. They even have an option to import your IMDb ratings to their database. Also, you can tweak the recommender as you wish to make it more personalized.
Most recommendation engines don't work for me and can't replace a good human curated list, but movielens.org makes surprisingly accurate predictions about my taste.
Another underrated source are user lists on mubi.com - Mubi has a very knowledgable community of enthusiasts and their lists cover really any topic, theme, genre and/or timeframe.
Lastly I'd recommend Year End Lists, particularly if you feel like you might have been missing out on good stuff or don't follow movies too closely. yearendlists.com aggregates year end lists from all kinds of respected sources, grouped by year (change the URL to look further back).
My advice would be to make a MovieLens account, spend the rest of 2018 putting in movies you've seen, and then you'll have a recommendation list for the whole year. I've been using it for a few years now, and it's pretty accurate about what it thinks I would like. It's also unearthed a lot of movies for me that I never would've watched on my own like In the Mood for Love, The Hunt, Stalker, Hunt for the Wilderpeople, Your Name, The Wailing, The Raid movies, that's all I can think of off the top of my head.
Thank you for your comments. Appreciate it especially from a standup comedian. Quite useful points and I agree with everything. Maybe except about recommendations based on IP - suggestions should work based on similar ratings amongst the users - the more you and everyone rate content, the better suggestion you get (e.g. like movielens.org works).
P.S. Is there some other "notable" stand-up action you have done (special, album, longer performance) and should be added to the page besides what there is already? If it isn't available on YouTube but on other platforms, that is OK as well. A more complete list of comedian content is the goal.
https://standupcomedy.world/steve-hofstetter
No problem. The Coursera recommender systems course is taught by the GroupLens faculty and you can get inspiration there too. Also, MovieLens is an actual movie recommendation site (https://movielens.org) using this data. You can see what they do and make your own version or try something new!
Then it's time to use a different site to tell you if you'll like a movie. I've been using UMn's MovieLens for over a decade. The students there use it to research statistics and relational databases.
Let me clarify. I would ideally like something like Movielens. Instead of inputting what I like or don't like or what I'm in the mood for. I want an engine that scans my current library see what I've watched and what i own and makes movie recommendations based on that criteria. Whether I already own said film or I need to go get it.
You have to rate movies you have watched. But the more important aspect of the site is that the algorithm predicts which movies you will like based on your previous ratings.
No rating system is perfect. IMDb is pretty good though. I admit that for all its brilliance R&M doesn't belong at #5... I mean above Simpsons? Above several damn fine dramas? Yeah right. But I believe IMDb ratings do reflect quality, if with a bias toward popularity or niche taste. Just look at the first couple pages and you'll see goods rise to the top. Some shows slip through the cracks, but show me a better site, I'm always looking for one. ( https://movielens.org has been real good to me as a recommendation engine )
Edit: Also I don't really like critics. They do too much thinking for me - I don't like knowing too much going in. And I don't agree w RT a lot. If I could find a site where I could rank movies and be set up with compatible critics, that however would be amazing.
Edit2: Sorry for rambling but... Notice we're mixing up series and movies. I'm not sure if each has their own trends or what.
Hmmm. Sounds intriguing though the set-up of the service (knowing my phone number or twitter name) is a little too personal for me. I tried the live chat but it says no one is around. edit: I'm not in the US though, that might be why you're not around?
I usually go to https://movielens.org/ for recommendation. It's a website developed and maintained by academics and purports to solve many of the problems you mentioned here partly by allowing users to add their own tags (thus addressing the Fantasia-for-adults issue). Can you tell me how your service might be better?
Movielens has been around for ages. Was originally set up as a cool college project by the University of Minnesota, and is still ad free. Load up your previously watched movies, rate them, and they answer this question for you.
>Did they silently shut down film recommendation engine jinni.com?
Nope. It was pretty clearly plastered on the site for about a month before it went down. The consumer facing site was taken down, as the technology is being implemented in future devices/services.
>For arguably the most famous (not the best really) dedicated film recommendation service there seems to be little to no coverage out there.
Yea, outside of Jinni.com, I didn't hear anything. A quick search reveals it was posted though.
>Was it really that insignificant, or am I missing something?
Indeed. Jinni was a small site. Not at all popular.
>And since they are gone, where do you go now (I mean IMDB's recommendation engine is as flawed as it comes).
I had a similar problem. Trackt has a shit recommendation engine (just recommending popular movies regardless of taste). IMDB works a bit, but really sucks. I recently found MovieLens. While it's not the best, it still works. And they have a "movie tuner" thing, where you can say "like this movie, but less/more X"
I say find one or a few critics you are most compatible with and only listen to them. If you're going to listen to critics - I prefer movie recommendation sites (Movie Lens!) cross ref'd with user reviews. (According to Movielens I'd rate J&J a 2/5. Yeah not gonna watch it anytime soon.)