Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Author:

Frej Jibril1ORCID,Shah Neel1ORCID,Knezevic Marta1ORCID,Nazaretsky Tanya1ORCID,Käser Tanja1ORCID

Affiliation:

1. EPFL, Switzerland

Publisher

ACM

Reference41 articles.

1. Solmaz Abdi, Hassan Khosravi, Shazia W. Sadiq, and Dragan Gasevic. 2020. Complementing educational recommender systems with open learner models. In LAK ’20: 10th International Conference on Learning Analytics and Knowledge, Frankfurt, Germany, March 23-27, 2020, Christoph Rensing and Hendrik Drachsler (Eds.). ACM, 360–365.

2. Neda Afreen, Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2023. Towards Explainable Educational Recommendation through Path Reasoning Methods. In Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023), Pisa, Italy, June 8-9, 2023(CEUR Workshop Proceedings, Vol. 3448), Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara (Eds.). CEUR-WS.org, 131–136. https://ceur-ws.org/Vol-3448/paper-29.pdf

3. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

4. Ludovico Boratto Gianni Fenu and Mirko Marras. 2019. The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses. 457–472.

5. Interplay between upsampling and regularization for provider fairness in recommender systems

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3. A Review of Explainable Recommender Systems Utilizing Knowledge Graphs and Reinforcement Learning;IEEE Access;2024

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