mlsanityData to 14 Oct 2026

An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms

arXiv 2107.07124 ↗First posted 15 Jul 2021cs.AIcited by 1

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Abstract (arXiv)

In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes. Our system consists of (1) a pseudo matching score module that provides reliable training labels; (2) a ranking model that scores every candidate teacher; (3) a novelty boosting module that gives additional opportunities to new teachers; and (4) a diversity metric that guardrails the recommended results to reduce the chance of collision. Offline experimental results show that our approach outperforms a wide range of baselines. Furthermore, we show that our approach is able to reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a five-month observation on a third-party online education platform.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

5 of 13 references.

References · 5

  1. 2016Wide & Deep Learning for Recommender Systemscited by 747
  2. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  3. 2020Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classescited by 4
  4. 2020Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approachcited by 3
  5. 2019A Multimodal Alerting System for Online Class Quality Assurancecited by 2

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
  2. 2024A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
  3. 2025Personalized Education with Ranking Alignment Recommendation
  4. 2024Course Recommender Systems Need to Consider the Job Market
  5. 2026RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender
  6. 2023Distillation from Heterogeneous Models for Top-K Recommendation
  7. 2025TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
  8. 2026SCOReD: Student-Aware CoT Optimization for Recommendation Distillation