Graph Neural Networks (GNNs) based recommendations have shown significant performance improvement by explicitly modeling the user-item interactions as a bipartite graph. However, the existing GNNs-based recommendation methods suffer from the over-smoothing problem caused by utilizing the uniform distance of the reception field. To address this issue, we propose to explicitly incorporate the higher-order smoothness information into the node representation learning, and propose a new GNNs-based recommendation model namedHigher-orderSmoothness enhancedGraphCollaborativeFiltering (HS-GCF). The proposed model is mainly composed of two parts, namely lower-order module and higher-order module. The lower-order module guarantees that the lower-order smoothness is well obtained by using the user-item interactions. The higher-order module uses the latent group assumption to restrict too much noise introduced by the uniform distance property, which we call the higher-order smoothness information. Experiments are conducted on three real-world public datasets, and the experimental results show the performance improvements compared with several state-of-the-art methods and verify the importance of explicitly incorporating the higher-order smoothness information into the node representation learning.

Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users’ preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.

Although recommender systems have achieved considerable success, sometimes it is difficult to convince users due to the failure to explain the recommendation results. For this reason, explainable recommender systems have drawn a lot of attention in recent years. Among explainable recommendation models, the meta-path-based model plays a significant role because it can reason over the path connecting a user–item pair to achieve explainability. However, it is difficult for the meta-path-based model to achieve such a common explanation in collaborative filtering as “a user similar to you has purchased item$A$” because there is no such meta-path. In this article, we contribute a new model named collaborative meta-path modeling for explainable recommendation (COMPER). It models the similarity of user pairs and item pairs through rating information and constructs collaborative meta-paths for explainability. In addition, we design an attention mechanism to aggregate different paths connecting the target user and the target item. Moreover, the information of the subgraph composed of all paths connecting the target user and the target item is integrated for rating prediction. Extensive experiments on five real-world datasets demonstrate that COMPER achieves good performance in a variety of scenarios, achieving improvements over several baselines.

Graph neural network (GNN) has shown its prominent performance in representation learning of graphs but it has not been fully considered for heterogeneous graphs which contain more complex structures and rich semantics. The rich semantic information of heterogeneous graph can be usually revealed by meta-paths. Therefore, most of the existing GNN models designed for heterogeneous graphs utilize the meta-path based neighborhood sampler to divide a heterogeneous graph into multiple homogeneous subgraphs according to various meta-paths so that the homogeneous GNN can be applied to investigate heterogeneous graphs. Nevertheless, the way of embedding semantic information of meta-paths into multiple homogeneous graphs isimplicitand ineffective, which cannot accurately capture the semantics of heterogeneous graphs. In this paper, we propose a novel semi-supervised GNN model namedExplicitMessage-Passing Heterogeneous Graph Neural Network (EMP), which executes the process ofexplicitmessage-passing along the meta-paths. Besides, we also propose a split method for meta-paths and consider mutual effect between various meta-paths in advance in the proposed model, so that the semantic information of the whole set of meta-paths can be captured accurately. Extensive experiments conducted on three real-world datasets demonstrate the superiority of the proposed model.

Most existing recommendation research has been concentrated on unidirectional recommendation, i.e. only recommending items to users. However, in many real-world scenarios, the platform needs to achieve bi-directional recommendation. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we first formalize a new recommendation problem called bi-directional recommendation and contribute a new bidirectional recommendation model named BiROR (Bi-directional Recommendation for Online Recruitment). In BiROR, an encoder component is utilized to learn the text embeddings, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendation. In the multi-task learning framework, we share the text embeddings and graph embeddings to alleviate the problems of data sparsity and data asymmetry in online recruitment. Extensive experiments in a real-world task show that BiROR outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model.