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

TL;DR

This paper introduces a meta-learning approach for recommender system algorithm selection that incorporates both user and algorithm features, including source code metrics, leading to improved performance.

Contribution

It presents a novel per-user meta-learning method that leverages algorithm characteristics derived from source code, enhancing selection accuracy in recommender systems.

Findings

Augmenting meta-learners with algorithm features improves NDCG@10 by 8.83%.

The approach outperforms the Single Best Algorithm baseline.

It closes 10.5% of the performance gap to an oracle selector.

Abstract

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.