# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
###### Abstract  
Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely lean complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items' neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms. heterogeneous information network; meta-path; neighbor information; attention mechanism; recommendation system; convolutional neural network +
Footnote †: journal:  
[PERSON] et al. [13] applied multi-attribute features other than a user ID and project ID, including user age and device category, and added an attention mechanism to distinguish the weights of paper titles and abstracts. With the popularity of deep learning (DL), collaborative filtering algorithms have also begun to use various DL models to obtain the representation of users and items, to make more efficient use of the historical interaction information between them, and to improve the performance and generalization ability of the recommendation system [14; 15; 16]. For example, [PERSON] et al. [17] proposed a combination of matrix decomposition and the multi-layer perceptron algorithm. [PERSON] et al. [18] proposed a GAN-based network embedding model to address the network sparsity problem. [PERSON] et al. [19] proposed the MMGRec model to learn nodes through graph attention networks of embedding features for recommendation. [PERSON] et al. [20] introduced an attention mechanism to effectively utilize review information. Heterogeneous information networks (HINs), as an emerging direction, can naturally model complex objects and their rich relationships in recommender systems [21]. Therefore, some researchers have started to realize the importance and necessity of HIN-based recommendations. [PERSON] et al. [22] proposed the OptRank approach to alleviating the cold-start problem by exploiting the heterogeneous information contained in social tags. Further, meta-path is widely used in learning recommendation algorithms based on heterogeneous information network representation, as it can extract rich semantic information. For example, [PERSON] et al. [23] proposed a heterogeneous information network embedding method called HERec, which only considers meta-paths whose starting type is user type or item type. [PERSON] et al. [24] proposed a contextual collaborative attention recommendation model, MCRec, based on rich meta-paths considering the influence between meta-paths and user-item pairs. Although meta-paths can extract semantics between nodes' information and feature information between nodes, they cannot fully extract information in the network. Therefore, [PERSON] et al. [25] proposed FMG, a meta-graph-based recommendation fusion framework on heterogeneous information networks. [PERSON] et al. [26] proposed an innovative convolutional neighborhood-based HIN recommendation interaction model, NIRec, which can capture and aggregate rich interaction information at the node and path levels. [PERSON] et al. [27] proposed a contrastive meta-learning framework on HINs named CM-HIN, which addresses the cold-start issue at both the data level and model level.