Problem
Research questions and friction points this paper is trying to address.
Aspect Affinity
eCommerce
User Preferences
Temporal Prediction
Personalization
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
🤖 AI Summary
本文通过使用小型语言模型DeepAffinity，从用户历史交互数据预测其对商品属性的长期偏好，以提高电商推荐、搜索和营销的个性化水平。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Small Language Models
Aspect Affinity
Temporal Prediction
Specialized Prediction Heads
Personalization
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