Research on color recipe recommendation based on unstructured data using TENN

TL;DR

This paper introduces TENN, a method that infers color recipes from unstructured emotional natural language data, addressing challenges in industries where color specifications are subjective and non-standardized.

Contribution

The paper presents TENN, a novel approach for extracting color recipes from unstructured natural language data, bridging the gap between human emotional descriptions and technical color specifications.

Findings

TENN successfully infers color recipes from emotional language.

The method improves application in industries relying on subjective color descriptions.

Demonstrated effectiveness in real-world scenarios involving color customization.

Abstract

Recently, services and business models based on large language models, such as OpenAI Chatgpt, Google BARD, and Microsoft copilot, have been introduced, and the applications utilizing natural language processing with deep learning are increasing, and it is one of the natural language preprocessing methods. Conversion to machine language through tokenization and processing of unstructured data are increasing. Although algorithms that can understand and apply human language are becoming increasingly sophisticated, it is difficult to apply them to processes that rely on human emotions and senses in industries that still mainly deal with standardized data. In particular, in processes where brightness, saturation, and color information are essential, such as painting and injection molding, most small and medium-sized companies, excluding large corporations, rely on the tacit knowledge and sensibility of color mixers, and even customer companies often present non-standardized requirements. . In this paper, we proposed TENN to infer color recipe based on unstructured data with emotional natural language, and demonstrated it.