🤖 AI Summary
This work addresses the limitation of conventional sequential recommendation models in capturing users’ sensory experiences. To bridge this gap, the authors propose ASEGR, a novel framework that uniquely integrates large language models with knowledge distillation to extract structured sensory attributes—such as color and scent—from product reviews. These attributes are distilled into fixed-dimensional sensory embeddings and seamlessly incorporated into mainstream sequential recommenders like SASRec and BERT4Rec. Extensive experiments on four Amazon datasets demonstrate that ASEGR significantly outperforms baseline methods in recommendation performance while generating interpretable sensory attributes aligned with human perception, thereby validating the efficacy of sensory semantics as a complementary signal for user behavior modeling.
📝 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, indicating that linguistically grounded sensory representations provide complementary signals to behavioral interaction patterns. Qualitative analysis further shows that the extracted attributes align closely with human perceptions of products, enabling interpretable connections between natural language descriptions and recommendation behavior. Overall, this work demonstrates that sensory attribute distillation offers a principled and scalable way to bridge information extraction and sequential recommendation through structured semantic representation learning.