Research on color recipe recommendation based on unstructured data using TENN
This addresses a domain-specific challenge for small and medium-sized companies in color-sensitive industries, but appears incremental as it applies existing NLP techniques to a new application area.
The paper tackled the problem of recommending color recipes from unstructured emotional language in industries like painting and injection molding, where small companies rely on human tacit knowledge, and demonstrated the proposed TENN method for this task.
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.