用协作权重筛选关键特征，让冷启动推荐更准更快。
Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
- 基于协同行为数据融合特征，提升表示能力。
- 仅用少量特征即达更高推荐准确率，计算开销更低。
- 适合资源受限的冷启动推荐场景，尤其对新用户/物品有效。
推荐系统中的冷启动问题需要利用用户-物品交互之外的辅助特征。然而，无关或噪声特征会降低预测性能，过多特征则增加计算负担，导致内存占用上升和训练时间延长。为此，我们提出一种特征选择策略，优先考虑用户行为信息。通过混合矩阵分解技术整合协同行为数据的相关性，增强特征表示，并采用最大体积算法机制进行特征排序。该方法识别出最具影响力的特征，在推荐准确率与计算效率之间取得平衡。我们在多个数据集和混合推荐模型上进行了广泛评估，结果表明，该方法在冷启动场景下能选出最小但最有效的特征子集。即使在严格减少特征数量的情况下，仍优于现有特征选择技术，且保持更高的效率。
原文摘要 · Abstract (English)
Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degrade predictive performance, whereas an excessive number of features increases computational demands, leading to higher memory consumption and prolonged training times. To address this, we propose a feature selection strategy that prioritizes the user behavioral information. Our method enhances the feature representation by incorporating correlations from collaborative behavior data using a hybrid matrix factorization technique and then ranks features using a mechanism based on the maximum volume algorithm. This approach identifies the most influential features, striking a balance between recommendation accuracy and computational efficiency. We conduct an extensive evaluation across various datasets and hybrid recommendation models, demonstrating that our method excels in cold-start scenarios by selecting minimal yet highly effective feature subsets. Even under strict feature reduction, our approach surpasses existing feature selection techniques while maintaining superior efficiency.
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