# Skill-based Explanations for Serendipitous Course Recommendation

arXiv: https://arxiv.org/abs/2508.19569 · HTML: https://mlsanity.com/paper/2508.19569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 126 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Empower Sequence Labeling with Task-Aware Neural Language Model](https://mlsanity.com/paper/1709.04109/) (2017)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System](https://mlsanity.com/paper/1907.01591/) (2019)
- [Personalized Course Sequence Recommendations](https://mlsanity.com/paper/1512.09176/) (2015)
- [Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance](https://mlsanity.com/paper/1803.09535/) (2018)
- [Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation](https://mlsanity.com/paper/1904.11798/) (2019)
- [Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization](https://mlsanity.com/paper/2402.08371/) (2024)

## Abstract (arXiv)

Academic choice is crucial in U.S. undergraduate education, allowing students significant freedom in course selection. However, navigating the complex academic environment is challenging due to limited information, guidance, and an overwhelming number of choices, compounded by time restrictions and the high demand for popular courses. Although career counselors exist, their numbers are insufficient, and course recommendation systems, though personalized, often lack insight into student perceptions and explanations to assess course relevance. In this paper, a deep learning-based concept extraction model is developed to efficiently extract relevant concepts from course descriptions to improve the recommendation process. Using this model, the study examines the effects of skill-based explanations within a serendipitous recommendation framework, tested through the AskOski system at the University of California, Berkeley. The findings indicate that these explanations not only increase user interest, particularly in courses with high unexpectedness, but also bolster decision-making confidence. This underscores the importance of integrating skill-related data and explanations into educational recommendation systems.