E-learning Recommender System Dataset
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
View Archive InfoField | Value | |
Title |
E-learning Recommender System Dataset
|
|
Identifier |
https://doi.org/10.7910/DVN/BMY3UD
|
|
Creator |
Hafsa, Mounir
|
|
Publisher |
Harvard Dataverse
|
|
Description |
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. 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).
Formatting and Encoding The dataset files are written as comma-separated values files with a single header row. Columns that contain commas (,) are escaped using double quotes ("). These files are encoded as UTF-8. User Ids 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). Item Ids 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). Ratings Data File Structure 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:
Item Data File Structure 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:
|
|
Subject |
Computer and Information Science
Recommender Systems, E-Learning, Mooc, Implicit, Explicit, Interactions, Ratings |
|
Language |
English
French |
|
Date |
2021-09-21
|
|
Contributor |
Hafsa, Mounir
|
|
Source |
Database
|
|