DOI: 10.7910/dvn/bmy3ud
title: E-learning Recommender System Dataset

description: &lt;p&gt;Mandarine Academy Recommender System (MARS) Dataset is captured from real-world open MOOC {https://mooc.office365-training.com/}. The dataset offers both explicit and implicit ratings, for both French and English versions of the MOOC. Compared with classical recommendation datasets like Movielens, this is a rather small dataset due to the nature of available content (educational). However, the dataset offers insights into real-world ratings and provides testing grounds away from common datasets.&lt;/p&gt;
&lt;p&gt;All items are available online for viewing in both French and English versions. All selected users had rated at least 1 item. No demographic information is included. Each user is represented by an id and job (if available).&lt;/p&gt;
&lt;p&gt;&lt;br /&gt;For both French and English, the same kind of files is available in .csv format. We provide the following files:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Users&lt;/strong&gt;: contains information about user ids and their jobs.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Items&lt;/strong&gt;: contains information about items (resources) in the selected language. Contains a mix of feature types.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Ratings&lt;/strong&gt;: Both &lt;span style="text-decoration: underline;"&gt;explicit&lt;/span&gt; (Watch time) and &lt;span style="text-decoration: underline;"&gt;implicit&lt;/span&gt; (page views of items).&lt;/li&gt;
&lt;/ul&gt;
&lt;h3&gt;&lt;strong&gt;Formatting and Encoding&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;The dataset files are written as &lt;a href="http://en.wikipedia.org/wiki/Comma-separated_values"&gt;comma-separated values&lt;/a&gt; files with a single header row. Columns that contain commas (,) are escaped using double quotes ("). These files are encoded as UTF-8.&lt;/p&gt;
&lt;h3&gt;&lt;strong&gt;User Ids&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;User ids are consistent between explicit_ratings.csv and implicit_ratings.csv and users.csv (i.e., the same id refers to the same user across the dataset).&lt;/p&gt;
&lt;h3&gt;&lt;strong&gt;Item Ids&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;Item ids are consistent between explicit_ratings.csv, implicit_ratings.csv, and items.csv (i.e., the same id refers to the same item across the dataset).&lt;/p&gt;
&lt;h3&gt;&lt;strong&gt;Ratings Data File Structure&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;All ratings are contained in the files explicit_ratings.csv and implicit_ratings.csv. Each line of this file after the header row represents one rating of one item by one user, and has the following format:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;item_id,user_id,created_at (implicit_ratings.csv)&lt;/li&gt;
&lt;li&gt;user_id,item_id,watch_percentage,created_at,rating (explicit_ratings.csv)&lt;/li&gt;
&lt;/ul&gt;
&lt;h3&gt;&lt;strong&gt;Item Data File Structure&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;Item information is contained in the file items.csv. Each line of this file after the header row represents one item, and has the following format:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;item_id,language,name,nb_views,description,created_at,Difficulty,Job,Software,Theme,duration,type&lt;/li&gt;
&lt;/ul&gt;