An Educational News Dataset for Recommender Systems

Author:

Xing Yujie,Mohallick Itishree,Gulla Jon Atle,Özgöbek Özlem,Zhang Lemei

Abstract

AbstractDatasets are an integral part of contemporary research on recommender systems. However, few datasets are available for conventional recommender systems and even very limited datasets are available when it comes to contextualized (time and location-dependent) News Recommender Systems. In this paper, we introduce an educational news dataset for recommender systems. This dataset is the refined version of the earlier published Adressa dataset and intends to support the university students in the educational purpose. We discuss the structure and purpose of the refined dataset in this paper.

Publisher

Springer International Publishing

Reference16 articles.

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2. Das, A.S., et al.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international Conference on World Wide Web, pp. 271–280. Association for Computing Machinery, Banff, Alberta, Canada (2007)

3. Gulla, J., et al.: The Intricacies of time in news recommendation. In: UMAP (2016)

4. Doctor, K.: Newsonomics: The New York Times puts personalization front and center—just For You (2019). https://www.niemanlab.org/2019/06/newsonomics-the-new-york-times-puts-personalization-front-and-center-just-for-you/. Accessed 19 May 2020

5. Kvalheim, H.: Norway’s first fully personalized mobile news site (2016)

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