## 4 Experiment  
### Experimental Design  
**Dataset**: The effectiveness of the proposed MIMA algorithm is evaluated by measuring its performance using three publicly available large datasets, i.e., MovieLens-1m [64], Amine, and Amazon-Book [65]. These three datasets have different properties, including size, domain, and sparsity, which are shown in detail in Table 1.  
**MovieLens-1m**: The anonymous users have a user ID, gender, age, occupation, and other attribute information, and movies include comedy, romance, action, and others; each user rates at least 20 movies. The training set, which consists of 80% of the interaction data, is 3.64 MB in size. The test set, comprising the remaining 20% of the data, is 940 KB. **Amine**: Each user can add animations to their completed list and rate them, and animations have the following attribute information: anime ID, anime name, genre, type, episodes, rating, and members. The training set for this dataset is 4.8 MB, while the test set is 1.2 MB. **Amazon-Book**: Book reviews consist of the following comment properties: review ID, title, price, user ID, review helpfulness, review score, and so on. Each book contains the following feature information: title, description, authors, categories, rating, and so on. The training set size is 901 MB, and the test set is 225 MB. Through the above explanation, we find that users and items have attribute information in the above datasets that is sufficient for building a heterogeneous graph containing features nodes. Then, we use a data preprocessing method to complete the optimization of the three datasets used by only retaining interaction data for which each user has at least 20 interactions with items. And then, 80% of the historical interaction data is randomly selected to form the training set, and the remaining historical interaction data are used as the test set. Meanwhile, each transaction record is a positive instance in the training set, and the negative sample is selected from the remaining items that the user has not interacted with before by using a random strategy. **Evaluation metrics**: The MIMA algorithm takes users, items, and their feature information as input to obtain the user preference scores for all items. In order to evaluate the performance of the output MIMA model's top-K recommendation and preference rankings, we use the following widely used evaluation metrics: Recall@K, where \(K\in\{20,30,50\}\), Precision@K, where \(K\in\{10,20\}\), and NDCG@K where \(K=20\). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \#Users & \#Items & \#Interactions & **Density** \\ \hline Amine & 15,506 & 34,325 & 2,601,998 & 0.00489 \\ MovieLens-1m & 6040 & 5953 & 1,000,209 & 0.04189 \\ Amazon-Book & 51,639 & 84,355 & 2,648,963 & 0.00056 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset statistics. **Baselines**: The proposed algorithm is compared with several classic baselines to analyze and evaluate the effectiveness of the MIMA: **ItemCF** (item-based collaborative filtering algorithm) [66], **MF** (matrix factorization algorithm) [67], **NCF** (neural collaborative filtering algorithm) [68], **NGCF** (neural graph collaborative filtering algorithm) [34], **PUP** (price-aware GCN algorithm) [69], **PinSAGE** (random-walk-based GCN algorithm) [70], **ATGCF** (attention-based GCF) [71], **ATGCN** (attention-based GCN) [72], **HetGNN**[15], and **HAN** (heterogeneous graph attention network) [14]. **Hyperparameters**: The embedding size for all models is within [32; 64; 128; 512]. The learning rate is set as \([0.0001,0.0005,0.001,0.005]\), the batch size is 4096, and there are three layers. The dropout for each layer is 0.1, and the maximum number of epochs is set to 100. It should be noted that during the training process, the users, items, and feature embeddings in the model are jointly trained. The dimension size of the graph convolution operator is the sum of the number of users, the number of items, and the number of features.