keys ())) fpath = cache (url = ml. Using pandas on the MovieLens dataset October 26, 2013 // python, pandas, sql, tutorial, data science. It has hundreds of thousands of registered users. 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. Dependencies (pip install): numpy pandas matplotlib TL;DR. For a more detailed analysis, please refer to the ipython notebook. 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. This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. It is recommended for research purposes. hive hadoop analysis map-reduce movielens-data-analysis data-analysis movielens-dataset hadoop-mapreduce mapreduce-java 10 million ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users. Work fast with our official CLI. 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. MovieLens Recommendation Systems. MovieLens 1B Synthetic Dataset. Getting the Data¶. It is changed and updated over time by GroupLens. The graph above shows that students tend to watch a lot of movies. The histogram shows the general distribution of the ratings for all movies. The dates generated were used to extract the month and year of the same for analysis purposes. As stated above, they can offer exclusive discounts to students to elevate their sales. Walmart can tie up with companies like Netflix or theatres and offer discounts to regular or loyal customers, thus improving sales on both sides. Note that these data are distributed as .npz files, which you must read using python and numpy. Using different transformations, it was combined to one file. Looking again at the MovieLens dataset, and the “10M” dataset, a straightforward recommender can be built. This data has been cleaned up - users who had less tha… This dataset was generated on October 17, 2016. These companies can promote or let students avail special packages through college events and other activities. DATA PRE-PROCESSING: Initially the data was converted to csv format for convenience sake. It has been cleaned up so that each user has rated at least 20 movies. The below scatter plots were produced by segregating only those movie ratings who have been rated more than 200 times. A correlation coefficient of 0.92 is very high and shows high relevance. 16.2.1. 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. Using the following Hive code, assuming the movies and ratings tables are defined as before, the top movies by average rating can be found: * Each user has rated at least 20 movies. Initially the data was converted to csv format for convenience sake. MovieLens 1B is a synthetic dataset that is expanded from the 20 million real-world ratings from ML-20M, distributed in support of MLPerf. Movie metadata is also provided in MovieLenseMeta. Movies with such ratings can be used to analyze upcoming movies of similar taste and to predict the crowd response on these movies. Right Figure: Make a scatter plot of men versus women and their mean rating for movies rated more than 200 times. Learn more. ... MovieLens 1M Dataset - Users Data. From the crrelation matrix, we can state the relationship between Occupation and Genres of Movies that an individual prefer. These are some of the special cases where difference in Rating of genre is greater than 0.5. MovieLens 1M movie ratings. 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, this class of population is a good target. Full MovieLens Dataset on Kaggle: Metadata for 45,000 movies released on or before July 2017. 4 different recommendation engines for the MovieLens dataset. After combining, certain label names were changed for the sake of convenience. For Example: Farmer do not prefer to watch Comedy|Mistery|Thriller and College Student Prefer Animation|Comedy|Thriller. This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. If nothing happens, download GitHub Desktop and try again. Most of the ratings lie between 2.5-5 which indicates the audience is generous. Use Git or checkout with SVN using the web URL. MovieLens | GroupLens 2. This implies that they are similar and they prove the analysis explained by the scatter plots. 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