Sensory-Aware Sequential Recommendation via Review-Distilled Representations
arXiv:2603.02709v1 Announce Type: new 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 representativ
arXiv:2603.02709v1 Announce Type: new 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.
Executive Summary
This article presents a novel framework, ASEGR, for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product reviews. The proposed method fine-tunes a large language model to extract structured sensory attribute-value pairs from unstructured review text and distills them into a compact student transformer. The learned sensory embeddings are incorporated into standard sequential recommender architectures, demonstrating improved performance across four Amazon domains. The extracted attributes align closely with human perceptions of products, enabling interpretable connections between natural language descriptions and recommendation behavior. This work offers a principled and scalable way to bridge information extraction and sequential recommendation through structured semantic representation learning.
Key Points
- ▸ ASEGR proposes a two-stage pipeline for extracting structured sensory attributes from product reviews
- ▸ The method fine-tunes a large language model to extract attribute-value pairs and distills them into a compact student transformer
- ▸ Sensory-enhanced models consistently outperform identifier-based counterparts across four Amazon domains
Merits
Strength
The proposed method offers a principled and scalable way to integrate sensory attributes into sequential recommendation, providing complementary signals to behavioral interaction patterns.
Improved Performance
ASEGR outperforms existing models in sequential recommendation tasks, demonstrating the effectiveness of linguistically grounded sensory representations.
Demerits
Limited Domain Specificity
The proposed method is evaluated on a limited set of Amazon domains, and its performance in other domains may vary.
Language Model Dependency
The success of ASEGR relies heavily on the performance of the underlying large language model, which may limit its applicability in scenarios where high-quality language models are not available.
Expert Commentary
The article presents a well-structured and well-executed study that demonstrates the potential of linguistically grounded sensory representations in sequential recommendation. The proposed method, ASEGR, shows impressive performance in integrating sensory attributes from product reviews, and its ability to extract interpretable connections between natural language descriptions and recommendation behavior is a significant contribution to the field. However, the method's dependency on high-quality language models and limited domain specificity are notable limitations that need to be addressed in future research. Overall, the study has implications for both practical applications and policy discussions, and it represents a valuable addition to the growing body of research on multimodal learning and explainability in AI.
Recommendations
- ✓ Future studies should explore the application of ASEGR in other domains and datasets to evaluate its generalizability and robustness.
- ✓ The development of more efficient and scalable methods for extracting sensory attributes from large-scale review data is essential for the widespread adoption of ASEGR.
