## 1 Introduction  
As consumer focus on food health and quality increases, developing tastier, healthier, and more popular food products is essential for attracting and retaining consumer attention and support [1]. The choice of food formulations and ingredients determines key elements such as taste, nutrition, and texture [2]. In the traditional food research and development (R&D) process, steps typically include formulation development, sample trial production, sample evaluation, small-scale trial production, sensory inspection of trial products, specialized customer tastings, and product identification. These steps are then followed by continuous adjustments based on market sales and consumer feedback, leading to further R&D improvements. This process demands significant time and financial investment. If the consumers' preferences can be identified early in product positioning and formulation development, targeted selection of ingredients can effectively reduce food development costs, increase development efficiency, and lower operational risks for enterprises. Current food R&D heavily relies on the rich experience of researchers, which arises not only from their years of study and work but also from various food science databases. Examples include the comprehensive knowledge base of food additive chemicals AdditiveChem [3] and the comprehensive food risk component database FRCD [4]. A common characteristic of these perspectives is that the knowledge describing ingredients often remains fragmented rather than organized in a semantic view that directly represents ingredient features and similarities among different ingredients. In fact, food formulations inherently contain information on the relationships between different ingredients. Mixture products in the market reflect the understanding of food R&D engineers regarding the nutritional and flavor characteristics of ingredients and their combinations in formulations. The pairing of different ingredients in the same food can precisely reflect their inherent compatibility. Food and ingredients can form a heterogeneous information network. In recent years, representation learning methods based on heterogeneous information networks have achieved significant results in solving complex network data mining problems [5; 6; 7]. These methods can help mine the semantic information of foods and ingredients, providing developers with a higher-dimensional perspective on food positioning and ingredients understanding. The selection of food ingredients and formulation design is directly related to consumer word-of-mouth. With the development of big data and natural language processing technologies, user ratings and review information on e-commerce platforms are playing an increasingly important role in obtaining consumer feedback [8; 9]. Ratings can directly reflect the degree of user preference for a product. However, the factors influencing consumer experience are diverse, including some aspects less related to the core product, such as price [10; 11], packaging [12; 13], and logistics [14; 15]. User ratings on e-commerce platforms often reflect the comprehensive experience of consumers throughout the entire purchase and usage process, which may not directly represent their preferences for the product itself. Therefore, for product developers, it is necessary to extract evaluations specifically related to product ingredients from user reviews, such as preferences for the ingredients themselves and perceptions of taste. This information can be used to form adjusted consumer feedback that excludes non-ingredient factors, providing a reference for the formulation development of food products. This paper aims to propose a research framework for investigating the relationship between food-ingredient characteristics and consumer word-of-mouth using heterogeneous information network representation.