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

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.02468 [cs.LG]

(or arXiv:2609.02468v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.02468 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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