###### Abstract  
Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for learning node embeddings, failing to leverage attribute information fully and to clarify the reasons behind a user's interest in specific items. A heterogeneous graph neural network for recommendation named MIMA (multi-feature interaction meta-path aggregation) is proposed to address these issues. Firstly, heterogeneous graphs consisting of user nodes, item nodes, and their feature nodes are constructed, and the meta-path containing users, items, and their attribute information is used to capture the correlations among different types of nodes. Secondly, MIMA integrates attention-based feature interaction and meta-path information aggregation to uncover structural and semantic information. Then, the constructed meta-path information is subjected to neighborhood aggregation through graph convolution to acquire the correlations between different types of nodes and to further facilitate high-order feature fusion. Furthermore, user and item embedding vector representations are obtained through multiple iterations. Finally, the effectiveness and interpretability of the proposed approach are validated on three publicly available datasets in terms of NDCG, precision, and recall and are compared to all baselines. heterogeneous graph; multi-head attention; multi-feature interaction; meta-path aggregation; heterogeneous graph neural network +
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attention network (GAT) [21] uses the attention strategy to perform aggregation operations on adjacent nodes to learn node feature representation. The performance of GCNs by aggregating neighbor features of different orders for each node has also been improved [22]. Hyperbolic graph neural networks (HGNNs) [23] are optimized to leverage the strategies of a distance-based self-attention mechanism and weighted aggregation. The above algorithms realize neighbor feature aggregation from spectral-based to hyperbolic-based. However, these approaches only consider the embedded representation of neighbor nodes for homogeneous graphs and do not integrate node attribute information, which results in under-utilization of the node's context information. A heterogeneous graph attention network (HAN) [14] utilizes node-level and semantic-level attention mechanisms and meta-path aggregation to achieve node embedding in heterogeneous graphs. A heterogeneous graph neural network (HetGNN) [15] uses HAN to aggregate attribute information of nodes to improve classification accuracy. Graph learning augmented heterogeneous graph neural networks (GL-HGNNs) [24] make full use of user-user relations, user-item interactions, and item-item similarities. The three graphs of feature similarity, feature propagation, and semantics are aggregated with the original heterogeneous graph, and the optimal graph is formed after iterative training in heterogeneous graph structure learning (HGSL) [25]. Graph structure and attribute information is extracted, and node representations are trained on the attribute information graph with semantic-aware heterogeneous network embedding (SHNE) [26]. Meta-path-based, relation-based, structure-attribute-enhanced heterogeneous graph learning algorithms have been proposed for learning node embedding. Although some algorithms consider node attribute information for node aggregation, they do not consider the influence of attributes for meta-path aggregation. Introducing node attributes in meta-path aggregation operations will facilitate the discovery of new recommendation rules and enhance the interpretability of recommendation results.