用小模型预测用户长期选品偏好，提升电商推荐精准度
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
- 用小语言模型+结构化提示词捕捉用户历史行为中的长期偏好
- 在跨国电商平台测试中显著优于通用大模型和常规微调方法
- 适合需要低延迟、高可解释性的电商个性化推荐场景
我们研究在电子商务中预测用户对品牌、尺寸、颜色等产品属性的偏好——这一任务称为「属性亲和性（Aspect Affinity）」。解决该任务可深化用户理解，实现推荐、搜索与营销的细粒度个性化。我们将属性亲和性建模为时序预测任务：从用户有序交互历史中预测其未来的属性选择，捕捉超越单次会话的长期偏好演变。为此，我们提出 DeepAffinity，利用小语言模型（SLMs）结合结构化提示词与专设预测头，并针对该任务进行微调。实验表明，DeepAffinity 在性能上优于标准生成式微调方法，而通用开源大模型在未进行任务特化微调时表现不佳，凸显其在建模细微行为上的局限性。最终，DeepAffinity 在大规模跨国电商平台上显著提升了推荐质量。
原文摘要 · Abstract (English)
We explore predicting eCommerce user preferences for product aspects such as brand, size, and color - a task we define as Aspect Affinity. Solving this task improves customer understanding and enables fine-grained personalization in recommendation, search, and marketing. We frame Aspect Affinity as a temporal prediction task: forecasting a users future aspect choices from their time-ordered interaction history, capturing long-term preferences that evolve beyond the current session. To this end, we propose DeepAffinity, which leverages Small Language Models (SLMs) with structured prompts and specialized prediction heads fine-tuned for this task. We show DeepAffinity outperforms standard generative fine-tuning methods, while general-purpose open-source LLMs perform poorly without task-specific tuning, highlighting their limits in modeling nuanced behavior. Finally, DeepAffinity enhances recommendation quality on a large-scale multinational eCommerce platform.
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