从梯度提升树到深度推荐器：迁移生产环境客户支持推荐器的实践经验
From Gradient-Boosted Trees to Deep Recommenders: Practical Lessons from Migrating a Production Customer Support Recommender
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中文总结 AI 辅助
本文介绍了将生产环境客户支持推荐系统从梯度提升树迁移到成对二元深度推荐器的实践，通过多种技术优化后，该方法在对话后期的推荐性能优于CatBoost基线。
中文摘要 AI 辅助
快速发展的服务型业务的产品目录正从静态、独立定价的SKU转向动态捆绑、折扣耦合的产品，这一转变对传统上用于稀疏且高度不平衡数据的树分类器造成了压力。这些分类器假设标签空间固定且变化缓慢，难以整合表格数据和对话记录等多模态信号。本文介绍了一个实时生产环境对话推荐系统从梯度提升多分类模型迁移到成对二元深度推荐器的过程。由于该系统对生态系统增长计划和下游功能（如动态推荐——在实时客户对话过程中向支持代理实时呈现最相关的推荐文本）至关重要，保持实时推荐质量是不可协商的约束条件。本文详细说明了使此次迁移成功的技术：将推荐任务重新表述为成对二元预测，以从用户和物品特征中联合学习，并通过负采样和噪声注入增强学习到的表示。为了高效整合长的实时对话上下文，本文对对话记录块应用了注意力池化，并将其与TF-IDF和句子嵌入基线进行了基准测试。最后，本文探索了多种架构（包括双塔模型、DeepFM及其变体）和损失函数（如对比损失）。在所有对话阶段与CatBoost基线进行评估后，本文证明所提方法在对话开始阶段达到了性能相当，且在后续对话阶段表现更优。
英文摘要
Product catalogs in fast-moving service businesses are shifting from static, independently priced SKUs toward dynamically bundled, discount-coupled offerings--a shift that strains the tree-based classifiers traditionally preferred for sparse and highly imbalanced data. These classifiers assume a fixed, slowly changing label space and struggle to incorporate multimodal signals such as tabular data and transcripts. We present the migration of a live, production conversational recommendation system from a gradient-boosted multiclass model to a pairwise-binary deep recommender. Because this system is critical to ecosystem growth initiatives and downstream features like dynamic pitching--surfacing the most relevant pitch text to a support agent in real time during a live customer conversation--maintaining live recommendation quality was a non-negotiable constraint. We detail the techniques that made this migration successful--reformulating recommendation as pairwise binary prediction to learn jointly from user and item features, and enhancing learned representations via negative sampling and noise injection. To efficiently incorporate long, live conversation context, we apply attention pooling over transcript chunks and benchmark it against TF-IDF and sentence-embedding baselines. Finally, we explore multiple architectures (including two-tower models, DeepFM, and their variants) and loss functions such as contrastive loss. Evaluating against a CatBoost baseline across all conversational stages, we demonstrate that our approach achieves parity at conversation beginning and outperforms at later conversational stages.
发表机构
- Intuit（英图易公司）
机构由 AI 辅助整理，请以论文原文为准。
