Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph data.Although existing HIN-based...Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph data.Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major limitations.Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the metapath.Besides,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered.To tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information network.Specifically,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified HIN.We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths.Extensive experiments on five real datasets demonstrate the effectiveness of our method.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61762078,62276073,61966009 and U22A2099)the Industrial Support Project of Gansu Colleges(No.2022CYZC11)+3 种基金the Natural Science Foundation of Gansu Province(21JR7RA114)the Northwest Normal University Young Teachers Research Capacity Promotion Plan(NWNU-LKQN2019-2)the Industrial Support Project of Gansu Colleges(No.2022CYZC11)the Northwest Normal University Post-graduate Research Funding Project(2021KYZZ02107).
文摘Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph data.Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major limitations.Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the metapath.Besides,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered.To tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information network.Specifically,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified HIN.We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths.Extensive experiments on five real datasets demonstrate the effectiveness of our method.
