DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models

arXiv:2609.02468v1 Announce Type: cross 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.