# Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics

arXiv: https://arxiv.org/abs/2508.04419 · HTML: https://mlsanity.com/paper/2508.04419/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization](https://mlsanity.com/paper/2008.13532/) (2020)
- [One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level](https://mlsanity.com/paper/1805.12118/) (2018)
- [Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance](https://mlsanity.com/paper/2410.09359/) (2024)
- [The Potential of AutoML for Recommender Systems](https://mlsanity.com/paper/2402.04453/) (2024)
- [Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems](https://mlsanity.com/paper/1912.08694/) (2019)
- [Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering](https://mlsanity.com/paper/2012.15151/) (2020)
- [Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets](https://mlsanity.com/paper/2409.05461/) (2024)
- [Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia](https://mlsanity.com/paper/1703.09108/) (2017)
- [Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps](https://mlsanity.com/paper/1703.09109/) (2017)
- [The Architecture of Mr. DLib's Scientific Recommender-System API](https://mlsanity.com/paper/1811.10364/) (2018)
- [Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]](https://mlsanity.com/paper/2006.12328/) (2020)

## Built on by (most cited first, 1 of 1)

- [Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems](https://mlsanity.com/paper/2604.07869/) (2026)

## Abstract (arXiv)

The Algorithm Selection Problem for recommender systems-choosing the best algorithm for a given user or context-remains a significant challenge. Traditional meta-learning approaches often treat algorithms as categorical choices, ignoring their intrinsic properties. Recent work has shown that explicitly characterizing algorithms with features can improve model performance in other domains. Building on this, we propose a per-user meta-learning approach for recommender system selection that leverages both user meta-features and automatically extracted algorithm features from source code. Our preliminary results, averaged over six diverse datasets, show that augmenting a meta-learner with algorithm features improves its average NDCG@10 performance by 8.83% from 0.135 (user features only) to 0.147. This enhanced model outperforms the Single Best Algorithm baseline (0.131) and successfully closes 10.5% of the performance gap to a theoretical oracle selector. These findings show that even static source code metrics provide a valuable predictive signal, presenting a promising direction for building more robust and intelligent recommender systems.