Work

A Distance-Based Multi-Model Fusion Method for Leaf Group Formula Raw Material Substitution Recommendation in Yunnan Cigarette Products

Year: 2025

Type: conference-paper

Abstract: This research aims to meet the practical needs of leaf group formula maintenance in cigarette production by utilizing machine learning to enhance the scientific and timely use of raw materials. A novel raw material substitution recommendation algorithm based on distance multi-model fusion is proposed. The algorithm comprises five spatial distance algorithm units and a voting decision mechanism, which together form a complete recommendation index mapping method. The results demonstrate that this method provides stable recommendation capabilities and offers a novel, efficient approach for formula maintenance personnel.

DOI: 10.1145/3729706.3729818

Authors: Qianxu Yang, Sheng Lei, Wei Zhang, Baokun Zhu, Mingzhan Lv, Xingdang Yang

Institutions: China Tobacco, China Tobacco Yunnan Industrial Co., Ltd. (China)

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Funder: China National Tobacco Corporation

OpenAlex ID: W4411887340

FWCI:

Cites: 1

Cited by:

Related to: 10