# A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

arXiv: https://arxiv.org/abs/2206.12291 · HTML: https://mlsanity.com/paper/2206.12291/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [Recommender Systems with Heterogeneous Side Information](https://mlsanity.com/paper/1907.08679/) (2019)
- [Adaptive Learning Material Recommendation in Online Language Education](https://mlsanity.com/paper/1905.10893/) (2019)
- [An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms](https://mlsanity.com/paper/2107.07124/) (2021)

## Built on by (most cited first, 2 of 2)

- [Adaptive and Personalized Exercise Generation for Online Language Learning](https://mlsanity.com/paper/2306.02457/) (2023)
- [Large Language Model Augmented Exercise Retrieval for Personalized Language Learning](https://mlsanity.com/paper/2402.16877/) (2024)

## Abstract (arXiv)

We propose a simple but effective method to recommend exercises with high quality and diversity for students. Our method is made up of three key components: (1) candidate generation module; (2) diversity-promoting module; and (3) scope restriction module. The proposed method improves the overall recommendation performance in terms of recall, and increases the diversity of the recommended candidates by 0.81% compared to the baselines.