Sensory-Aware Sequential Recommendation via Review-Distilled Representations
Sequential recommenders learn behavioral patterns from item identifiers, while the experiential properties that users describe in reviews, such as how products look, feel, smell, taste, or sound, rarely enter item representations in a controlled, auditable form. We present ASER (Attribute-based Sensory-Enhanced Representation), an offline pipeline that fine-tunes a large language model to extract evidence-grounded sensory attribute-value records, such as color: matte black or scent: vanilla, from review text and distills them into a compact student encoder that produces a frozen five-facet sensory bank for each item catalog. At recommendation time the pretrained backbone stays frozen: a l...
| Authors | Yeo Chan Yoon, Chanjun Park, Kyuhan Koh |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 2 October 2026 |
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