Name
Abstract | ||
|---|---|---|
Recommender-system datasets are used for recommender-system offline evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music, there are rather few datasets from research-paper recommender systems. In this paper, we introduce RARD, the Related-Article Recommendation Dataset, from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based filtering, stereotype, most popular), what types of features were used in content based filtering (simple terms vs. keyphrases), where the features were extracted from (title or abstract), and the time when recommendations were delivered and clicked. In addition, the dataset contains an implicit item-item rating matrix that was created based on the recommendation click logs. RARD enables researchers to train machine learning algorithms for research-paper recommendations, perform offline evaluations, and do research on data from Mr. DLibu0027s recommender system, without implementing a recommender system themselves. In the field of scientific recommender systems, our dataset is unique. To the best of our knowledge, there is no dataset with more (implicit) ratings available, and that many variations of recommendation algorithms. The dataset is available at http://data.mr-dlib.org, and published under the Creative Commons Attribution 3.0 Unported (CC-BY) license. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1045/july2017-beel | D-Lib Magazine |
DocType | Volume | Issue |
Journal | abs/1706.03428 | 7/8 |
ISSN | Citations | PageRank |
D-Lib Magazine, Vol. 23, No. 7/8. Publication date: July 2017 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jöran Beel | 1 | 65 | 9.60 |
| Zeljko Carevic | 2 | 24 | 4.29 |
| Johann Schaible | 3 | 65 | 11.02 |
| Gábor Neusch | 4 | 0 | 1.01 |