RARD II: The 94 Million Related-Article Recommendation Dataset

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Title: RARD II: The 94 Million Related-Article Recommendation Dataset
Authors: Beel, JoeranSmyth, BarryCollins, Andrew
Permanent link: http://hdl.handle.net/10197/10888
Date: 30-Jun-2018
Online since: 2019-07-11T11:24:53Z
Abstract: The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from Mr. DLib, a recommender-system as-a-service in the digital library and reference-management-software domain. As such, RARD II complements datasets from other domains such as books, movies, and music. The dataset encompasses 94m recommendations, delivered in the two years from September 2016 to September 2018. The dataset covers an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual (implicit) ratings matrices, RARD II includes the original recommendation logs, which provide a unique insight into many aspects of the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approaches for predicting algorithm performance. In this paper, we summarise the key features of this dataset release, describe how it was generated and discuss some of its unique features. Compared to its predecessor RARD, RARD II contains 64% more recommendations, 187% more features (algorithms, parameters, and statistics), 50% more clicks, 140% more documents, and one additional service partner (JabRef).
Funding Details: Enterprise Ireland
Science Foundation Ireland
Type of material: Conference Publication
Publisher: CEUR-WS.org
Start page: 39
End page: 55
Keywords: Recommender systems datasetsDigital librariesClick logs
DOI: 10.xxx
Other versions: http://amir-workshop.org/2019/05/22/proceedings-of-the-1st-interdisciplinary-workshop-on-algorithm-selection-and-meta-learning-in-information-retrieval-amir2019/
Language: en
Status of Item: Peer reviewed
Is part of: Beel, J., Kotthoff, L. (eds.). Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)
Conference Details: AMIR2019: 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval, Cologne, Germany, April 14 2019
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