用算法代码特征提升推荐系统选型效果
Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics
- 基于用户和算法代码特征做元学习选型
- 平均NDCG@10提升8.83%至0.147
- 适合构建智能推荐系统的研究者
推荐系统中的算法选择问题——为特定用户或场景挑选最优算法——仍是一个重大挑战。传统元学习方法常将算法视为类别选项，忽略其内在特性。近期研究表明，在其他领域显式建模算法特征可提升模型性能。受此启发，我们提出一种面向用户的元学习推荐算法选择方法，同时利用用户元特征与从源代码自动提取的算法特征。在六个不同数据集上的初步结果表明，加入算法特征后，元学习器的平均NDCG@10性能从仅使用用户特征时的0.135提升至0.147，改善8.83%。该增强模型优于单一最佳算法基线（0.131），并缩小了10.5%的性能差距至理论最优选择器。结果表明，即使静态源代码度量也具有重要预测价值，为构建更鲁棒、智能的推荐系统提供了新方向。
原文摘要 · Abstract (English)
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.
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