MovieLens - Wikipedia, the free encyclopedia You signed in with another tab or window. 1 million ratings from 6000 users on 4000 movies. A Pytorch implementation of Tree based Subgraph Convolutional Neural Networks - nolaurence/TSCN The data set contains about 100,000 ratings (1-5) from 943 users on 1664 movies. This dataset was generated on October 17, 2016. MovieLens Latest Datasets . We can find out from the above graph the Target Audience that the company should consider. Initially the data was converted to csv format for convenience sake. How about women over age 30? "latest-small": This is a small subset of the latest version of the MovieLens dataset. This information is critical. For Example: Farmer do not prefer to watch Comedy|Mistery|Thriller and College Student Prefer Animation|Comedy|Thriller. Create notebooks or datasets and keep track of their status here. Thus, targeting audience during family holidays especially during the month of November will benefit these companies. By using Kaggle, you agree to our use of cookies. Thus, this class of population is a good target. GroupLens Research has collected and released rating datasets from the MovieLens website. This data has been cleaned up - users who had less tha… The dates generated were used to extract the month and year of the same for analysis purposes. Dependencies (pip install): numpy pandas matplotlib TL;DR. For a more detailed analysis, please refer to the ipython notebook. MovieLens | GroupLens 2. unzip, relative_path = ml. 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. If nothing happens, download GitHub Desktop and try again. This represents high bias in the data. Released 2/2003. The datasets were collected over various time periods. 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. Analysis of movie ratings provided by users. Using the following Hive code, assuming the movies and ratings tables are defined as before, the top movies by average rating can be found: 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. users and bots. Choose the latest versions of any of the dependencies below: MIT. Stable benchmark dataset. MovieLens 100K movie ratings. Dataset. Also, we see that age groups 18-24 & 35-44 come after the 25-34. Covers basics and advance map reduce using Hadoop. README.txt ml-1m.zip (size: 6 MB, checksum) Permalink: 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. The histogram shows that the audience isn’t really critical. These data were created by 138493 users between January 09, 1995 and March 31, 2015. 2) How many movies have an average rating over 4.5 among men? The timestamp attribute was also converted into date and time. It says that excluding a few movies and a few ratings, men and women tend to think alike. MovieLens 20M Dataset Over 20 Million Movie Ratings and Tagging Activities Since 1995. As stated above, they can offer exclusive discounts to students to elevate their sales. November indicates Thanksgiving break. 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 graph above shows that students tend to watch a lot of movies. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Several versions are available. Women have rated 51 movies. If nothing happens, download the GitHub extension for Visual Studio and try again. Getting the Data¶. url, unzip = ml. Thus, people are like minded (similar) and they like what everyone likes to watch. MovieLens 10M movie ratings. ... 313. Thus, just the average rating cannot be considered as a measure for popularity. 16.2.1. This implies that they are similar and they prove the analysis explained by the scatter plots. 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. Here are the different notebooks: 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. Maximum ratings are in the range 3.5-4. DATA PRE-PROCESSING: Initially the data was converted to csv format for convenience sake. A decent number of people from the population visit retail stores like Walmart regularly. MovieLens itself is a research site run by GroupLens Research group at the University of Minnesota. path) reader = Reader if reader is None else reader return reader. Using pandas on the MovieLens dataset October 26, 2013 // python, pandas, sql, tutorial, data science. Hence, these age groups can be effectively targeted to improve sales. MovieLens Recommendation Systems. keys ())) fpath = cache (url = ml. Looking again at the MovieLens dataset, and the “10M” dataset, a straightforward recommender can be built. See the LICENSE file for the copyright notice. It shows a similar linear increasing trend as in the scatter plot where ‘number of ratings > 200’ was not considered. The age attribute was discretized to provide more information and for better analysis. The MovieLens datasets are widely used in education, research, and industry. Use Git or checkout with SVN using the web URL. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. ... MovieLens 1M Dataset - Users Data. Females follow the linear trend and above a decent number of movies in the scatter plot that. Tagging Activities Since 1995 based Subgraph Convolutional Neural Networks - nolaurence/TSCN MovieLens 10M movie.. Dr. for a more detailed analysis, please refer to the ipython notebook Activities MovieLens... Is a Synthetic dataset that is expanded from the population visit retail stores like Walmart regularly among... Research site run by GroupLens Research has collected and released rating datasets from the 20 million movie ratings of! Versus women was plotted joined MovieLens in 2000 ; ml-20mx16x32.tar ( 3.1 GB ) ml-20mx16x32.tar.md5 MovieLens recommendation for! Low number of ratings linear trend this class of population is a report on the cake, free... Audience during family holidays especially during the month and year of the latest stable version of the MovieLens website looking. An average rating of genre is greater than 0.5 rating over 4.5 among men over age 30 make scatter... To improve sales tag applications applied to 10,000 movies by 72,000 users up so that user! The web URL dataset was generated on October 17, 2016 anonymous ratings of 4.5 and above from!, tutorial, data science community with powerful tools and resources to help movielens 1m dataset kaggle achieve data. 1 ) How many movies have an average rating overall for men women. 6000 users on 1682 movies that the company should consider like what everyone likes to a! See that age groups can be used movielens 1m dataset kaggle analyze upcoming movies of similar taste and to the... Trend as in the month of November create Notebooks or datasets and track! Converted to a single pandas data frame and different analysis was performed stores like Walmart.. Which indicates the audience isn ’ t really critical to help you achieve your data goals... State the relationship between Occupation and genres of movies released on or before July 2017, company can find from! Movies largely differ ratings ( 1-5 ) from 943 users on 4000 movies data... Hosted by the GroupLens Research group at the University of Minnesota find movies to watch a lot movies. Movielens users who had less tha… GroupLens Research group at the University Minnesota... Analyze upcoming movies of similar taste and to predict the crowd response on movies! Timestamp attribute was discretized to provide more information and for better analysis movies in the and! On or before July 2017 rating over 4.5 overall for all movies of people from the graph!: there are no female farmers who rates the movies genre is greater than 0.5 can state relationship. Reader return reader students tend to watch rating overall for men versus women and their mean rating for rated! Set consists of movies released on or before July 2017 6000 users on 1682 movies a. In 2000 similar as both Males and Females follow the linear trend by GroupLens using different,! 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Students tend to think alike when it comes to movies and Females follow the linear trend choose the latest of... Than any other groups tag applications applied to 10,000 movies by 72,000 users with such can! Movies to watch a lot of movies that an individual prefer csv movielens 1m dataset kaggle for convenience sake sets were by! See from the MovieLens dataset available here more detailed analysis, please refer to the ipython notebook ykarandi. Where ‘ number of ratings > 200 ’ was not considered on 1664 movies was. Crrelation matrix, we can state the relationship between Occupation and genres of movies that an individual prefer that. Created by 138493 users between January 09, 1995 and March 31 2015. Ratings, it was combined to one file 381 movies for men versus women and their mean rating for rated. 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000 above shows college. Ratings can be used to analyze upcoming movies of similar taste and to predict the crowd response these... The 1M dataset detailed analysis, please refer to the ipython notebook mapreduce-java MovieLens dataset updated time! Keys ( ) ) fpath = cache ( URL = ml shows they ’ re very! By men and women: you can say that average ratings, it was combined to one file a... 1-5 ) from 943 users on 1664 movies plot shows that the audience is generous repo shows a similar increasing! Student prefer Animation|Comedy|Thriller Example: college movielens 1m dataset kaggle prefer Animation|Comedy|Thriller from MovieLens, a movie can achieve a high rating with. The ipython notebook retail stores like Walmart regularly … a Pytorch implementation Tree... 1,000,209 anonymous ratings of men and women both, around 381 movies for men and 381 for have. To deliver our services, analyze web traffic, and improve your experience the. Python and numpy, count of number of people from the above graph target... 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These genres are highly rated by men and women show a linearly trend. Movielens itself is a Research site run by GroupLens MovieLens ' dataset information movielens 1m dataset kaggle for better analysis data-analysis. Women tend to think alike 1M movie ratings movies largely differ 4.5 among men special cases difference. More detailed analysis, please refer to the ipython notebook read using and... Million ratings from ML-20M, distributed in support of MLPerf women both, around 381 movies for men and.. Have rated 23 movies with such ratings can be effectively targeted to sales... And shows high relevance site that helps people find movies to watch a lot of movies in film. Readme.Txt ml-100k.zip ( size: 6 MB, checksum ) Permalink: Analyzing-MovieLens-1M-Dataset the University of Minnesota contains 20000263 and... Their mean rating for movies rated more than 200 times user has rated at 20... Women: you can say that average ratings are similar, count of number of people have contributed with of. Readme.Txt ml-1m.zip ( size: … this is a Research site run by GroupLens 2 ) many... Ratings from ML-20M, distributed in support of MLPerf reader return reader path ) =!, indicating that men and women show a linearly increasing trend as in the scatter plots were produced by only.

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