Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics
This work addresses the challenge of choosing the best algorithm for specific users in recommender systems, offering an incremental improvement over traditional meta-learning methods.
The paper tackled the algorithm selection problem in recommender systems by proposing a meta-learning approach that uses both user meta-features and algorithm features extracted from source code, resulting in an 8.83% improvement in average NDCG@10 performance from 0.135 to 0.147 across six datasets.
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.