Recommender Systems for Learning

Manouselis, Nikos; Drachsler, Hendrik; Verbert, Katrien; Duval, Erik

doi:10.1007/978-1-4614-4361-2

Cited by 109 publications

(78 citation statements)

References 35 publications

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“…First, they usually do not work well when the user feedback data is sparse, which is often the case in the educational domain [1]. Second, they are only limited to k neighbours for each user.…”

Section: A Graph-based Recommender Systemmentioning

confidence: 99%

“…They have become increasingly popular because of their successful applications in the e-commerce field, such as with Amazon and eBay. Recommender systems have been introduced in the educational domain as a practical solution to help users find suitable content that can support their learning process [1], [2]. Traditionally, recommender systems have been evaluated according to accuracy metrics in the Information Retrieval area.…”

Section: Introductionmentioning

confidence: 99%

“…Martin [7] claimed in his keynote to the ACM RecSys 2009 conference that around 50% of a recommender's commercial success goes to the aspects of "Human-Recommender Interaction" while the algorithm matters for 5% only (Martin2009). The importance of the user perspective has been realized even more in the educational domain [1], [8], [9]. Indeed, the main goal of the educational recommender systems extends well beyond accurate predictions and should also take into account quality metrics such as usefulness, novelty, or diversity of the recommendations.…”

Section: Introductionmentioning

confidence: 99%

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User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

Fazeli

Drachsler

Bitter-Rijpkema

et al. 2018

IEEE Trans. Learning Technol.

Self Cite

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Abstract-Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should be also performed before deploying a recommender system to a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. We conduct both a data-centric evaluation and a user-centric evaluation on the same data collected from an authentic social learning platform. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights on how candidate algorithms perform on each of the five quality metrics: usefulness, accuracy, novelty, diversity, and serendipity of the recommendations.

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Section: A Graph-based Recommender Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

Fazeli

Drachsler

Bitter-Rijpkema

et al. 2018

IEEE Trans. Learning Technol.

Self Cite

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“…These efforts resulted in a number of interesting observations as described in [66]: 1) There is a significant increase of recommender systems applied in TEL due to the digitalisation of learning and the growths of educational data. 2)…”

Section: Technology Enhanced Learning (Tel)mentioning

confidence: 99%

“…The chapter is based on a previous study by Manouselis, Drachsler, Vuorikari, Hummel, Koper in 2011 [65] in the first Recommender System Handbook, and a Springerbriefs book from 2012 by Manouslis, Drachsler, Verbert, and Duval [66].…”

Section: Introductionmentioning

confidence: 99%