How to load the MovieLens dataset in Python
February 3, 2026 at 4:54 AM
How to load the MovieLens dataset in Python
Nice. I'm going to go answer this blind and see how different. It also reminds me of movielens dot org, which tracks your movie ratings across genres like, history, documentary, war, western, music, mystery, etc (to name the ones that, weirdly, are my high scores).
September 25, 2026 at 3:14 PM
Nice. I'm going to go answer this blind and see how different. It also reminds me of movielens dot org, which tracks your movie ratings across genres like, history, documentary, war, western, music, mystery, etc (to name the ones that, weirdly, are my high scores).
I let movielens . org mine my movie preferences- the data is interesting. I've rated 472 movies across their 19 overlapping genres. My average rating is 3.4 and I average ~72 ratings per genre. Some more than others-- 195 in action, 1 each in Documetary and TV Movie.
#moviesky
#moviesky
September 25, 2026 at 4:05 PM
I let movielens . org mine my movie preferences- the data is interesting. I've rated 472 movies across their 19 overlapping genres. My average rating is 3.4 and I average ~72 ratings per genre. Some more than others-- 195 in action, 1 each in Documetary and TV Movie.
#moviesky
#moviesky
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
June 13, 2026 at 1:00 PM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
A good research path may be recreating a recommender system (MovieLens has tons of publicly available ratings). A distribution of preferences is a similar data structure as other opinions.
February 10, 2024 at 11:10 PM
A good research path may be recreating a recommender system (MovieLens has tons of publicly available ratings). A distribution of preferences is a similar data structure as other opinions.
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
February 24, 2025 at 3:00 AM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
April 3, 2025 at 5:00 AM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
June 30, 2025 at 10:00 PM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
March 30, 2025 at 10:00 PM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
May 6, 2025 at 8:00 AM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
May 26, 2025 at 2:00 PM
movielens_100k: MovieLens 100K (1998).
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies.
24129 nodes, 95580 edges.
https://networks.skewed.de/net/movielens_100k
Ridiculogram:
I am tempted to simply reject papers based on the fact that they still experiment with Movielens 100K dataset o.O
November 17, 2025 at 12:35 PM
I am tempted to simply reject papers based on the fact that they still experiment with Movielens 100K dataset o.O
I use movielens.org for movie recommendations. For many months it has predicted I would love "Your Name".
This month, the Criterion Channel added "Your Name" to its streaming service. I watched it recently, and movielens was right. It was great!
www.criterionchannel.com/interdimensi...
This month, the Criterion Channel added "Your Name" to its streaming service. I watched it recently, and movielens was right. It was great!
www.criterionchannel.com/interdimensi...
Your Name. - Interdimensional Romance - The Criterion Channel
Directed by Makoto Shinkai • 2016 • Japan
Starring Ryunosuke Kamiki, Mone Kamishiraishi, Ryo Narita
Mitsuha and Taki are complete strangers living separate lives until they suddenly switch places. Mi...
www.criterionchannel.com
February 14, 2024 at 4:12 AM
I use movielens.org for movie recommendations. For many months it has predicted I would love "Your Name".
This month, the Criterion Channel added "Your Name" to its streaming service. I watched it recently, and movielens was right. It was great!
www.criterionchannel.com/interdimensi...
This month, the Criterion Channel added "Your Name" to its streaming service. I watched it recently, and movielens was right. It was great!
www.criterionchannel.com/interdimensi...
For anyone that has seen this circulating around: all the data in the article is drawn from MovieLens, a site established in 2024 that just replicates TMDB but with strange discrepancies. Also the article author specialises in marketing start-up sites. www.statsignificant.com/p/which-movi...
Which Movies Do People Love to Hate? A Statistical Analysis
Which films and actors are famous for being bad?
www.statsignificant.com
March 13, 2025 at 4:18 PM
For anyone that has seen this circulating around: all the data in the article is drawn from MovieLens, a site established in 2024 that just replicates TMDB but with strange discrepancies. Also the article author specialises in marketing start-up sites. www.statsignificant.com/p/which-movi...
Here's my ratings (movielens). I watch a lot of bad movies on purpose, but if it's "so bad it's good" I still rate them bad
January 7, 2025 at 11:54 PM
Here's my ratings (movielens). I watch a lot of bad movies on purpose, but if it's "so bad it's good" I still rate them bad
Vibed a simple gui for migrating my ratings/watched dates from MovieLens over to Letterboxd
Im wondering if atproto would be a good data protocol for a movie review/ratings social app.
Im wondering if atproto would be a good data protocol for a movie review/ratings social app.
February 23, 2026 at 7:49 AM
Vibed a simple gui for migrating my ratings/watched dates from MovieLens over to Letterboxd
Im wondering if atproto would be a good data protocol for a movie review/ratings social app.
Im wondering if atproto would be a good data protocol for a movie review/ratings social app.
Homework 4 EECS 349 Answered
1) Collaborative Filtering: Looking at the Data (2 points) When doing machine learning, it is important to have an understanding of the dataset that you will be working on. On the course website under “links” there is a link to the MovieLens dataset. You will use the…
1) Collaborative Filtering: Looking at the Data (2 points) When doing machine learning, it is important to have an understanding of the dataset that you will be working on. On the course website under “links” there is a link to the MovieLens dataset. You will use the…
Homework 4 EECS 349 Answered
1) Collaborative Filtering: Looking at the Data (2 points) When doing machine learning, it is important to have an understanding of the dataset that you will be working on. On the course website under “links” there is a link to the MovieLens dataset. You will use the MovieLens 100K dataset to build a collaborative filter to predict the rating a user may give to a movie they haven’t seen.
jarviscodinghub.com
September 8, 2026 at 12:54 PM
Homework 4 EECS 349 Answered
1) Collaborative Filtering: Looking at the Data (2 points) When doing machine learning, it is important to have an understanding of the dataset that you will be working on. On the course website under “links” there is a link to the MovieLens dataset. You will use the…
1) Collaborative Filtering: Looking at the Data (2 points) When doing machine learning, it is important to have an understanding of the dataset that you will be working on. On the course website under “links” there is a link to the MovieLens dataset. You will use the…
Bridging Conversational and Collaborative Signals for Conversational Recommendation
New dataset Reddit-ML32M improves conversational recommendation systems by combining Reddit conversations with MovieLens 32M interactions.
Read more: https://arxiv.org/html/2412.06949v1
New dataset Reddit-ML32M improves conversational recommendation systems by combining Reddit conversations with MovieLens 32M interactions.
Read more: https://arxiv.org/html/2412.06949v1
December 13, 2024 at 9:42 AM
Bridging Conversational and Collaborative Signals for Conversational Recommendation
New dataset Reddit-ML32M improves conversational recommendation systems by combining Reddit conversations with MovieLens 32M interactions.
Read more: https://arxiv.org/html/2412.06949v1
New dataset Reddit-ML32M improves conversational recommendation systems by combining Reddit conversations with MovieLens 32M interactions.
Read more: https://arxiv.org/html/2412.06949v1
My slides for the #reneuir workshop at #sigir2025 are now at: https://djoerdhiemstra.com/2025/on-the-neural-hype-and-improving-efficiency-of-sparse-retrieval/
On the Neural Hype and Improving Efficiency of Sparse Retrieval
“The Neural Hype, Justified!” exclaimed Jimmy Lin in an opinion paper in the SIGIR Forum of December 2019. But is it really? Effectiveness-wise, maybe not: I will share some recent examples that show that neural rankers on new data do not even significantly improve a weak sparse baseline. If they do improve on old data, some neural rankers have been pre-trained on the test data – the ultimate sin of the machine learning professional – convincingly shown for the MovieLens data in the SIGIR 2025 poster of Dario Di Palma and colleagues: “Do LLMs Memorize Recommendation Datasets?” Efficiency-wise, neural rankers are no match to sparse rankers. The standard BERT (re-)ranker hailed by Lin’s SIGIR Forum paper may be as much as 10 million times as inefficient as a sparse ranker (Yes, you read that right). I will show some recent innovations for improving the efficiency of sparse rankers: The score-fitted index and the constant-length index (a SIGIR 2025 poster too!) which are implemented in Zoekeend, a new experimental search engine based on the relational database engine DuckDB and available from: https://gitlab.science.ru.nl/informagus/zoekeend/
_Presented at the SIGIR Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2025)_
[download slides]
djoerdhiemstra.com
July 18, 2025 at 5:32 AM
My slides for the #reneuir workshop at #sigir2025 are now at: https://djoerdhiemstra.com/2025/on-the-neural-hype-and-improving-efficiency-of-sparse-retrieval/
Re discrepancies: MovieLens reports 5,541 ratings for Battlefield Earth. These most certainly did not come from users so must be pulled elsewhere. However IMDb has 84k ratings and TMDB has 860 ratings. So where the fuck did they come from?
March 13, 2025 at 4:22 PM
Re discrepancies: MovieLens reports 5,541 ratings for Battlefield Earth. These most certainly did not come from users so must be pulled elsewhere. However IMDb has 84k ratings and TMDB has 860 ratings. So where the fuck did they come from?
UofMN has a movie rating website as a research project. I like it. Recommends movies. A few years ago they sent email to all users using cc not bcc. Just trash email acct for me, but that's a big no-no
MovieLens
movielens.org
December 30, 2024 at 4:03 AM
UofMN has a movie rating website as a research project. I like it. Recommends movies. A few years ago they sent email to all users using cc not bcc. Just trash email acct for me, but that's a big no-no
LLM4Rec merges large language models with multimodal data, causal debiasing and explainable outputs, achieving 2.3% higher NDCG@10 and 1.4% diversity gain on benchmarks like MovieLens‑25M. https://getnews.me/llm4rec-advances-multimodal-ai-recommendation-with-causal-debiasing/ #llm4rec #multimodal
October 3, 2025 at 9:31 PM
LLM4Rec merges large language models with multimodal data, causal debiasing and explainable outputs, achieving 2.3% higher NDCG@10 and 1.4% diversity gain on benchmarks like MovieLens‑25M. https://getnews.me/llm4rec-advances-multimodal-ai-recommendation-with-causal-debiasing/ #llm4rec #multimodal
Incidentally, if anyone wants to mess with it, that one is fun
Still need to fix some bits. But fun
Still need to fix some bits. But fun
Movie Universe: 31,000+ Films Visualized
Interactive visualization of 31,000+ movies from 1920-2023. Year vs average rating scatter with co-rating network overlay showing how films are connected by shared viewers. Data: MovieLens 32M.
dr.eamer.dev
March 30, 2026 at 8:20 PM
Incidentally, if anyone wants to mess with it, that one is fun
Still need to fix some bits. But fun
Still need to fix some bits. But fun