[2603.02709] Sensory-Aware Sequential Recommendation via Review-Distilled Representations

Computer Science > Computation and Language arXiv:2603.02709 (cs) [Submitted on 3 Mar 2026] Title:Sensory-Aware Sequential Recommendation via Review-Distilled Representations Authors:Yeo Chan Yoon View a PDF of the paper titled Sensory-Aware Sequential Recommendation via Review-Distilled Representations, by Yeo Chan Yoon View PDF HTML (experimental) Abstract:We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product reviews. Our approach, \textsc{ASEGR} (Attribute-based Sensory Enhanced Generative Recommendation), introduces a two-stage pipeline in which a large language model is first fine-tuned as a teacher to extract structured sensory attribute--value pairs, such as \textit{color: matte black} and \textit{scent: vanilla}, from unstructured review text. The extracted structures are then distilled into a compact student transformer that produces fixed-dimensional sensory embeddings for each item. These embeddings encode experiential semantics in a reusable form and are incorporated into standard sequential recommender architectures as additional item-level representations. We evaluate our method on four Amazon domains and integrate the learned sensory embeddings into representative sequential recommendation models, including SASRec, BERT4Rec, and BSARec. Across domains, sensory-enhanced models consistently outperform their identifier-based counterparts, indicati...