DeepAffinity用小模型预测电商用户长期偏好，比大模型更精准，已落地提升推荐效果。
DeepAffinity利用小型语言模型预测用户对产品品牌、尺寸、颜色等方面的长期偏好。该模型通过结构化提示和专用预测头进行微调，在基准测试中优于标准生成式微调方法。研究显示通用开源大模型缺乏任务特定调优时表现不佳，难以捕捉细微行为模式。该系统已在大型跨国电商平台提升推荐质量。
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
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.