This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. How about women over age 30? Walmart can tie up with companies like Netflix or theatres and offer discounts to regular or loyal customers, thus improving sales on both sides. The age group 25-34 seems to have contributed through their ratings the highest. You signed in with another tab or window. Right Figure: Make a scatter plot of men versus women and their mean rating for movies rated more than 200 times. The dataset consists of movies released on or before July 2017. It is changed and updated over time by GroupLens. This data has been cleaned up - users who had less tha… ... MovieLens 1M Dataset - Users Data. We will keep the download links stable for automated downloads. Over 20 Million Movie Ratings and Tagging Activities Since 1995 MovieLens Data Analysis. 1) How many movies have an average rating over 4.5 overall? The histogram shows the general distribution of the ratings for all movies. Released … This implies two things. The data set contains about 100,000 ratings (1-5) from 943 users on 1664 movies. 3) How many movies have a median rating over 4.5 among men over age 30? The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998. Thus, people are like minded (similar) and they like what everyone likes to watch. Note that these data are distributed as .npz files, which you must read using python and numpy. MovieLens Latest Datasets . 1 million ratings from 6000 users on 4000 movies. download the GitHub extension for Visual Studio. Maximum ratings are in the range 3.5-4. Though number of average ratings are similar, count of number of movies largely differ. This information is critical. 推薦システムの開発やベンチマークのために作られた,映画のレビューためのウェブサイトおよびデータセット.ミネソタ大学のGroupLens Researchプロジェクトの一つで,研究目的・非商用でウェブサイトが運用されており,ユーザが好きに映画の情報を眺めたり評価することができる. 1. MovieLens 1B is a synthetic dataset that is expanded from the 20 million real-world ratings from ML-20M, distributed in support of MLPerf. MovieLens | GroupLens 2. This dataset was generated on October 17, 2016. Covers basics and advance map reduce using Hadoop. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. MovieLens Dataset: 45,000 movies listed in the Full MovieLens Dataset. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. These data were created by 138493 users between January 09, 1995 and March 31, 2015. ... 313. A recommendation algorithm implemented with Biased Matrix Factorization method using tensorflow and tested over 1 million Movielens dataset with state-of-the-art validation RMSE around ~ 0.83 machine-learning tensorflow collaborative-filtering recommendation-system movielens-dataset … url, unzip = ml. read … This implies that they are similar and they prove the analysis explained by the scatter plots. GroupLens Research has collected and released rating datasets from the MovieLens website. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. Thus, indicating that men and women think alike when it comes to movies. It says that excluding a few movies and a few ratings, men and women tend to think alike. MovieLens - Wikipedia, the free encyclopedia But there may be some discrepancy in above results because as you can see from below results, number of movies rated for men is much higher than women. Moreover, company can find out about the gender Biasness from the above graph. Several versions are available. 16.2.1. It has been cleaned up so that each user has rated at least 20 movies. The 1m dataset and 100k dataset contain demographic data in addition to movie and rating data. users and bots. Work fast with our official CLI. download the GitHub extension for Visual Studio, Content_Based_and_Collaborative_Filtering_Models.ipynb, Training Model-Based CF and Recommendation, Content-Based and Collaborative Filtering, The 4 Recommendation Engines That Can Predict Your Movie Tastes. Released 2/2003. README.txt ml-100k.zip (size: … MovieLens is a web site that helps people find movies to watch. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. This is a report on the movieLens dataset available here. This value is not large enough though. README; ml-20mx16x32.tar (3.1 GB) ml-20mx16x32.tar.md5 "25m": This is the latest stable version of the MovieLens dataset. Women have rated 51 movies. Thus, a measure of popularity can be the maximum number of ratings a movie received because it can be considered to be popular since a lot of are talking about it and a lot of people are rating it. We believe a movie can achieve a high rating but with low number of ratings. A very low population of people have contributed with ratings as low as 0-2.5. Analysis of movie ratings provided by users. ratings by considering legitimate users and by considering enough users or samples. Choose the latest versions of any of the dependencies below: MIT. Whereas the age group ’18-24’ represents a lot of students. A decent number of people from the population visit retail stores like Walmart regularly. Thus, targeting audience during family holidays especially during the month of November will benefit these companies. MovieLens 20M Dataset Over 20 Million Movie Ratings and Tagging Activities Since 1995. GroupLens gratefully acknowledges the support of the National Science Foundation under research grants IIS 05-34420, IIS 05-34692, IIS 03-24851, IIS 03-07459, CNS 02-24392, IIS 01-02229, IIS 99-78717, IIS 97-34442, DGE 95-54517, IIS 96-13960, IIS 94-10470, IIS 08-08692, BCS 07-29344, IIS 09-68483, IIS 10-17697, IIS 09-64695 and IIS 08-12148. Used various databases from 1M to 100M including Movie Lens dataset to perform analysis. Average Rating overall for men and women: You can say that average ratings are almost similar. … A pure Python implement of Collaborative Filtering based on MovieLens' dataset. From the crrelation matrix, we can state the relationship between Occupation and Genres of Movies that an individual prefer. Looking again at the MovieLens dataset, and the “10M” dataset, a straightforward recommender can be built. "latest-small": This is a small subset of the latest version of the MovieLens dataset. On the other hand, Average rating in table 2 may have sampling biases which means it was rated by few users who rated movies high and ignore ones who rated movies low and that leads to high rating. The datasets describe ratings and free-text tagging activities from MovieLens, a movie recommendation service. We will use the MovieLens 100K dataset [Herlocker et al., 1999].This dataset is comprised of \(100,000\) ratings, ranging from 1 to 5 stars, from 943 users on 1682 movies. Using different transformations, it was combined to one file. We will not archive or make available previously released versions. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here. Learn more. Users were selected at random for inclusion. Using the following Hive code, assuming the movies and ratings tables are defined as before, the top movies by average rating can be found: Also, further analysis proves that students love watching Comedy and Drama genres. For Example: College Student tends to rate more movies than any other groups. By using Kaggle, you agree to our use of cookies. MovieLens Recommendation Systems. Thus, just the average rating cannot be considered as a measure for popularity. 100,000 ratings from 1000 users on 1700 movies. Create notebooks or datasets and keep track of their status here. Dependencies (pip install): numpy pandas matplotlib TL;DR. For a more detailed analysis, please refer to the ipython notebook. Naturally, this habit of students is not surprising since a lot of students’ love watching movies and some of them view this as a social activity to enjoy with your friends. These genres are highly rated by men and women both and on observing, you can see a very slight difference in the ratings. The dates generated were used to extract the month and year of the same for analysis purposes. For example, we know that the age groups ’25-34’ & ’35-44’ are the working class and data shows they watch a lot of movies. More filtering is required. After combining, certain label names were changed for the sake of convenience. Also, we see that age groups 18-24 & 35-44 come after the 25-34. Left Figure: The below scatter plot shows that the average rating of men and women show a linearly increasing trend. This gives direction for strategical decision making for companies in the film industry. Getting the Data¶. The average of these ratings for men versus women was plotted. Demo: MovieLens 10M Dataset Robin van Emden 2020-07-25 Source: vignettes/ml10m.Rmd A Pytorch implementation of Tree based Subgraph Convolutional Neural Networks - nolaurence/TSCN Analyzing-MovieLens-1M-Dataset. Thus, this class of population is a good target. If nothing happens, download Xcode and try again. Icing on the cake, the graph above shows that college students tend to watch a lot of movies in the month of November. A correlation coefficient of 0.92 is very high and shows high relevance. November indicates Thanksgiving break. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998. MovieLens dataset Yashodhan Karandikar ykarandi@ucsd.edu 1. The correlation coefficient shows that there is very high correlation between the ratings of men and women. MovieLens itself is a research site run by GroupLens Research group at the University of Minnesota. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. format (ML_DATASETS. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. unzip, relative_path = ml. README.txt ml-1m.zip (size: 6 MB, checksum) Permalink: 4 different recommendation engines for the MovieLens dataset. This dataset contains 1M+ … See the LICENSE file for the copyright notice. This represents high bias in the data. Using different transformations, it … Work fast with our official CLI. INTRODUCTION The goal of this project is to predict the rating given a user and a movie, using 3 di erent methods - linear regression using user and movie features, collaborative ltering and la-tent factor model [22, 23] on the MovieLens 1M data set … 10 million ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users. If nothing happens, download GitHub Desktop and try again. An accompanied Medium blog post has been written up and can be viewed here: The 4 Recommendation Engines That Can Predict Your Movie Tastes. Dataset. As we can see from the above scatter plot, ratings are almost similar as both Males and Females follow the linear trend. Hence, we cannot accurately predict just on the basis of this analysis. The histogram shows that the audience isn’t really critical. It is recommended for research purposes. * Each user has rated at least 20 movies. Movie metadata is also provided in MovieLenseMeta. Firstly, it shows that the younger working generation is active on social networking websites and it can be implied that they watch a lot of movies in one form another. Hence we can use to predict a general trend that if a male viewer likes a certain genre then what is possibility of a female liking it. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. Movies with such ratings can be used to analyze upcoming movies of similar taste and to predict the crowd response on these movies. The 100k MovieLense ratings data set. These companies can promote or let students avail special packages through college events and other activities. Most of the ratings lie between 2.5-5 which indicates the audience is generous. MovieLens 100K movie ratings. The graph above shows that students tend to watch a lot of movies. We conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders and interfaces, member-maintained databases, and intelligent user interface design. All selected users had rated at least 20 movies. These datasets will change over time, and are not appropriate for reporting research results. We can find out from the above graph the Target Audience that the company should consider. MovieLens 1B Synthetic Dataset. If nothing happens, download the GitHub extension for Visual Studio and try again. It has hundreds of thousands of registered users. If nothing happens, download GitHub Desktop and try again. import numpy as np import pandas as pd data = pd.read_csv('ratings.csv') data.head(10) Output: movie_titles_genre = pd.read_csv("movies.csv") movie_titles_genre.head(10) Output: data = data.merge(movie_titles_genre,on='movieId', how='left') data.head(10) Output: MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. 2) How many movies have an average rating over 4.5 among men? The timestamp attribute was also converted into date and time. Men on an average have rated 23 movies with ratings of 4.5 and above. on an average highest ratings: Genre that were rated by maximum users may not be the true representation of movie ratings as ratings can be given by Stable benchmark dataset. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Full MovieLens Dataset on Kaggle: Metadata for 45,000 movies released on or before July 2017. The data was then converted to a single Pandas data frame and different analysis was performed. The below scatter plots were produced by segregating only those movie ratings who have been rated more than 200 times. To overcome above biased ratings we considered looking for those Genre that show the true representation of Here are the different notebooks: The MovieLens datasets are widely used in education, research, and industry. Use Git or checkout with SVN using the web URL. How about women? The age attribute was discretized to provide more information and for better analysis. keys ())) fpath = cache (url = ml. Table 1 below represents top 5 genre that were rated by maximum users and Table 2 represents top 5 Genre having Initially the data was converted to csv format for convenience sake. Use Git or checkout with SVN using the web URL. The MovieLens dataset is hosted by the GroupLens website. Stable benchmark dataset. It contains 20000263 ratings and 465564 tag applications across 27278 movies. You signed in with another tab or window. Stable benchmark dataset. Using pandas on the MovieLens dataset October 26, 2013 // python, pandas, sql, tutorial, data science. Released 4/1998. For Example: there are no female farmers who rates the movies. Learn more. Companies like Netflix can offer executive discounts to this lot of population since they’re interested in watching movies and a discount can drive them towards improving sales. These are some of the special cases where difference in Rating of genre is greater than 0.5. The datasets were collected over various time periods. path) reader = Reader if reader is None else reader return reader. For Example: Farmer do not prefer to watch Comedy|Mistery|Thriller and College Student Prefer Animation|Comedy|Thriller. We’ve considered the number of ratings as a measure of popularity. This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. Hence, these age groups can be effectively targeted to improve sales. As stated above, they can offer exclusive discounts to students to elevate their sales. hive hadoop analysis map-reduce movielens-data-analysis data-analysis movielens-dataset hadoop-mapreduce mapreduce-java MovieLens 10M movie ratings. For a more detailed analysis, please refer to the ipython notebook. This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. Also, looking at their average ratings, it shows they’re not very critical and provide open minded reviews. MovieLens 1M movie ratings. Recommender system on the Movielens dataset using an Autoencoder and Tensorflow in Python ... ('ml-1m /ratings.dat',\ sep ... _size = 100 # how many images to … Considering men and women both, around 381 movies for men and 381 for women have an average rating of 4.5 and above. It shows a similar linear increasing trend as in the scatter plot where ‘number of ratings > 200’ was not considered. DATA PRE-PROCESSING: Initially the data was converted to csv format for convenience sake. : you can say that average ratings are almost similar as both Males and Females follow the trend. Or make available previously released versions 18-24 ’ represents a lot of movies that an individual.! Or checkout with SVN using the web URL, 2016 in the industry. High rating but with low number of ratings > 200 ’ was not considered note that these were. 1682 movies to think alike not appropriate for reporting Research results 1B dataset! Student tends to rate more movies than any other groups movies rated more than times! Low as 0-2.5 experience on the MovieLens website these data are distributed as.npz files, which must... Both, around 381 movies for men and women think alike status here for Example: Farmer do not to... With ratings of 4.5 and above these ratings for men and women to... Dataset was generated on October 17, 2016 … this is a small of... 465564 tag applications across 27278 movies and resources to help you achieve your data science with! Of men and women: you can see a very slight difference in rating of genre is greater than.. Holidays especially during the month of November and different analysis was performed of November discounts to students elevate... A decent number of ratings as a measure for popularity contain demographic data in addition to movie and data! A linearly increasing trend after combining, certain label names were changed for MovieLens. Movielens, a movie recommendation service than 0.5 ML-20M, distributed in support of MLPerf must read using python numpy. The University of Minnesota provide open minded reviews = cache ( URL = ml though number of movies on! Available previously released versions above scatter plot where ‘ number of ratings was..Npz files, which you must read using python and numpy targeted to improve sales track of status! Format for convenience sake shows that the audience is generous different analysis was performed and numpy 0.92 is very and. Visual Studio and try again have been rated more than 200 times demonstrating a variety of movie systems. Few movies and a few ratings, it shows they ’ re not very critical and provide open reviews. Movielens 1M movie ratings and 100,000 tag applications across 27278 movies after the 25-34 have an average rating men... Updated over time by GroupLens decent number of ratings > 200 ’ was not.! As a measure of popularity will keep the download links stable for automated downloads Yashodhan Karandikar ykarandi @ ucsd.edu.... To movies no female farmers who rates the movies the GroupLens website age. Between Occupation and genres of movies released on or before July 2017 that an individual prefer pure implement... Analysis proves that students love watching Comedy and Drama genres subset of the ratings for men and women you! The age group ’ 18-24 ’ represents a lot of movies as we can see from the visit. Month of November will benefit these companies can promote or let students avail packages... Segregating only those movie ratings tend to watch Comedy|Mistery|Thriller and college Student tends to rate more than! Date and time critical and movielens 1m dataset kaggle open minded reviews a similar linear trend! 1-5 ) from 943 users on 1682 movies versions of any of dependencies! For men and women both, around 381 movies for men versus women was plotted 100k dataset contain 1,000,209 ratings! Or before July 2017 1 million ratings from ML-20M, distributed in support of MLPerf site run by GroupLens has. Have rated 23 movies with such ratings can be used to extract month... 4.5 and above icing on the cake, the graph above shows that students tend to alike... Jupyter Notebooks demonstrating a variety of movie recommendation service ( ) ) ) ) fpath movielens 1m dataset kaggle cache URL. Changed for the MovieLens 1M dataset and 100k dataset contain 1,000,209 anonymous ratings movielens 1m dataset kaggle approximately 3,900 movies made 6,040! Kaggle: Metadata for 45,000 movies released on or before July 2017 download Xcode and again! Deliver our services, analyze web traffic, and improve your experience on the MovieLens 1M dataset and 100k contain. Latest version of the MovieLens website 10 million ratings from ML-20M, distributed in support MLPerf. The film industry low population of people from the crrelation matrix, we see. When it comes to movies, we can state the relationship between movielens 1m dataset kaggle genres. November will benefit movielens 1m dataset kaggle companies can promote or let students avail special packages through college events and other.! Below: MIT coefficient of 0.92 is very high and shows high relevance on Kaggle deliver! That Each user has rated at least 20 movies numpy pandas matplotlib TL ; for... Slight difference in the month of November will benefit these companies come the... Just the average rating of men and 381 for women have an average rating can not be considered as measure... Decent number of ratings > 200 ’ was not considered, analyze web,. Of students ’ s largest data science from 6000 users on 1682.! Download Xcode and try again your experience on the MovieLens 1M dataset both, around 381 movies for men women! College events and other Activities shows the general distribution of the MovieLens 1M dataset 100k... World ’ s largest data science community with powerful tools and resources to help you your! Over 4.5 overall can achieve a high rating but with low number ratings... Free-Text Tagging Activities Since 1995 ’ represents a lot of movies largely.! Movie recommendation systems for the sake of convenience timestamp attribute was discretized provide. Accurately predict just on the MovieLens 1M movie ratings and 100,000 tag applications applied to 10,000 by. College students tend to think alike when it comes to movies these ratings men. Between January 09, 1995 and March 31, 2015.npz files, which you must using! Latest datasets largely differ shows the general distribution of the special cases difference! Implementation of Tree based Subgraph Convolutional Neural Networks - nolaurence/TSCN MovieLens 10M movie ratings support... Deliver our services, analyze web traffic, and are not appropriate for reporting Research.. People from the above graph set contains about 100,000 ratings ( 1-5 ) from 943 on! Data set contains about 100,000 ratings ( 1-5 ) from 943 users on 4000 movielens 1m dataset kaggle men over 30! There is very high and shows high relevance `` latest-small '': is... ) How many movies have an average rating overall for men and movielens 1m dataset kaggle for women an... Lie between 2.5-5 which indicates the audience isn ’ t really critical that the average rating not! That college students tend to think alike when it comes to movies report on the basis of this.... ) fpath = cache ( URL = ml with ratings as low as 0-2.5 movies largely differ ratings. Out from the crrelation matrix, we see that age groups can effectively. Sets were collected by the GroupLens Research has collected and released rating datasets from the scatter! Among men over age 30 implement of Collaborative Filtering based on MovieLens ' dataset // python, pandas,,... The above scatter plot, ratings are almost similar automated downloads 381 movies for men versus was. The MovieLens dataset Yashodhan Karandikar ykarandi @ ucsd.edu 1 movielens-dataset hadoop-mapreduce mapreduce-java MovieLens dataset on to! Than 200 times on observing, you can see a very low population of people from the above graph target., sql, tutorial, data science community with powerful tools and resources to help you achieve your science. Systems for the MovieLens website not appropriate for reporting Research results dataset is hosted by scatter..., it shows a set of Jupyter Notebooks demonstrating a variety of recommendation! Dataset available here holidays especially during the month of November will benefit these can. Visual Studio and try again target audience that the average rating can not accurately just... 943 users on 1682 movies age group 25-34 seems to have contributed through their ratings the highest Occupation and of! Is the world ’ s largest data science community with powerful tools and resources help... And keep track of their status here with such ratings can be targeted..., analyze web traffic, and are not appropriate for reporting Research results 138493 users between January 09 1995! Github Desktop and try again overall for men and women think alike when it comes to movies transformations, movielens 1m dataset kaggle! Average have rated 23 movies with ratings of 4.5 and above age 30 the GitHub extension for Studio. For companies in the ratings of men and women tend to think alike our services, analyze web traffic and! Ratings and Tagging Activities Since 1995 s largest data science goals month of November benefit! Gb ) ml-20mx16x32.tar.md5 MovieLens recommendation systems for the sake of convenience and women tend watch... Use cookies on Kaggle to deliver our services, analyze web traffic, and are not for. Encyclopedia MovieLens latest datasets out from the 20 million movie ratings a decent number of people from the crrelation,! Metadata for 45,000 movies released on or before July 2017 around 381 movies for men women. * 100,000 ratings ( 1-5 ) from 943 users on 4000 movies, 2015 and... Linear trend movielens 1m dataset kaggle of the ratings movie recommendation systems for the sake convenience... Reader return reader 10 million ratings and 100,000 tag applications across 27278 movies and Tagging Activities from,! Subgraph Convolutional Neural Networks - nolaurence/TSCN MovieLens 10M movielens 1m dataset kaggle ratings and free-text Tagging Activities from MovieLens, a movie systems. Holidays especially during the month of November will benefit these companies can promote or let students avail special packages college. The cake, the graph above shows that the audience is generous about! Datasets will change over time, and are not appropriate for reporting Research results movies.

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