An NLP-Based Perfume Note Estimation Based on Descriptive Sentences

Abstract

The perfume industry is a suitable candidate for applying advanced natural language processing techniques, yet most existing studies focus on developing fragrance design systems based on artificial intelligence advances. To meet the increasing demand for analyzing and exploiting descriptive sentences for the fragrance market, we investigate the relationship between descriptive sentences of perfumes and their notes in this paper. Our purpose for this investigation is to build a core idea for a perfume recommendation system of descriptive sentences. To accomplish this, we propose a system for perfume note estimation of descriptive sentences based on several sentence transformer models. In our leave-one-out cross-validation tests using our dataset containing 62 perfumes and 255 perfume notes, we achieved significant performance improvements (from a 37.1∼41.1% to 72.6∼79.0% hit rate with the top five items, and from a 22.1∼31.9% to a 57.3∼63.2% mean reciprocal rank) for perfume note estimation via our fine-tuning process. In addition, some qualitative examples, including query descriptions, estimated perfume notes, and the ground truth perfume notes, are presented. The proposed system improves the perfume note estimation performances using a fine-tuning process on a newly constructed dataset containing descriptive sentences of perfumes and their notes.

Keywords

• natural language processing
• perfume note estimation
• sentence embedding
• STS fine-tuning