## 3 Recommended Architecture for Industrial Software Service Components  
The following describes the proposed framework for industrial software service component recommendation, as shown in Figure 1. The framework applies to the scenario of a typical industrial collaborative manufacturing system based on the proposed explicit and implicit higher-order feature interactions and attentional factorization machine model. In this scenario, end-node devices are encapsulated in a microservice style to form service components and invoked through APIs such as sensor components, robotic arm components, conveyor components, etc. Features such as component descriptive information, prevalence, and categories are stored by the edge nodes, which transmit the component feature information to the cloud, while the staff transmit features such as descriptive information and categories of the industrial tasks to the cloud. The cloud repository not only stores the above two kinds of feature information, it stores the combination relationship between historical industrial tasks and service components. The description information of service components and industrial tasks in the cloud repository is preprocessed, usually in the steps of stemming, normalization, tagging, and stop-word deletion, then the processed results are used as inputs to the Doc2 Vec [36] model. It is precisely because Doc2 Vec is designed to learn embeddings for entire documents or paragraphs that it is more suitable for capturing document-level semantic information, such as descriptions of industrial tasks and service components. The textual representation of the description information is transformed into a vectorial representation, based on which the similarity between the industrial task description information and the service component description information are calculated using the cosine similarity. Subsequently, additional relevant attributes, including factors such as the popularity of industrial service components, the category of industrial service components, and industrial tasks, are retrieved from the cloud industrial service database to form a feature matrix. In summary, the feature matrix consists of vector representations of relevant feature attributes of service components and industrial tasks, such as categories, description statements, similarities, etc. This matrix is then processed and serves as the training data for the EIAFM model. The EIAFM model has five components: a prediction component, implicit component, attention component, explicit component, and linear component. The linear component discerns the direct relationship among the input and the goal, the implicit component is used to capture the information about the implicit higher order feature intersections, the explicit component is used to capture the information about the explicit higher-order feature intersections, the attention component is used to quantify the contributions of different second-order feature interactions to the prediction results, and the prediction component finally sums the initial four outputs and estimates the recommendation probability using a sigmoid function. The model's performance is evaluated using the AUC and log-loss metrics during the training process. The reason for using the AUC value is because it can robustly measure the model's ability to identify positive and negative samples under different threshold settings. When using the EIAFM model trained with data on historical industrial tasks and service component combination relationships, the framework recommends the top-k ranked industrial based on staff members' descriptions of the industrial task requirements and software service components with the highest probability. Combination of Explicit and Implicit Higher-Order Feature Interaction and Attentional Factorization Machines  
In this segment, we first outline the problem definition for the recommendation of industrial software service components related to industrial tasks.