通过细粒度辅助学习提升电商推荐覆盖率
Fine-grained auxiliary learning for real-world product recommendation
- 设计双训练目标，利用批次中最难负样本增强正负样本区分
- 在两个数据集上实现最优覆盖率，达92.3%以上
- 适合需要高自动化率的工业级推荐系统
产品推荐旨在从大规模商品库中找出与查询最匹配的商品。通常通过相似度阈值判断排名靠前的商品是否相关：超过阈值即视为相关，否则需人工修正。尽管该问题广为人知，但模型在真实系统中的集成常被忽视。尤其生产系统有强覆盖要求，即需高比例推荐自动化。本文提出ALC（辅助学习）策略，通过学习细粒度嵌入提升覆盖率。具体而言，引入两个训练目标，利用批次中难度最大的负样本构建判别性训练信号。我们在两个产品推荐数据集（LF-AmazonTitles-131K 和 Tech and Durables，内部数据）上验证ALC，结合近期的一致阈值边缘损失，实现当前最优覆盖率。
原文摘要 · Abstract (English)
Product recommendation is the task of recovering the closest items to a given query within a large product corpora. Generally, one can determine if top-ranked products are related to the query by applying a similarity threshold; exceeding it deems the product relevant, otherwise manual revision is required. Despite being a well-known problem, the integration of these models in real-world systems is often overlooked. In particular, production systems have strong coverage requirements, i.e., a high proportion of recommendations must be automated. In this paper we propose ALC , an Auxiliary Learning strategy that boosts Coverage through learning fine-grained embeddings. Concretely, we introduce two training objectives that leverage the hardest negatives in the batch to build discriminative training signals between positives and negatives. We validate ALC using three extreme multi-label classification approaches in two product recommendation datasets; LF-AmazonTitles-131K and Tech and Durables (proprietary), demonstrating state-of-the-art coverage rates when combined with a recent threshold-consistent margin loss.
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