基于完整超圖神經(jīng)網(wǎng)絡(luò)的捆綁推薦模型
打開文本圖片集
中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1001-3695（2025）07-011-2003-08
doi：10.19734/j.issn.1001-3695.2024.11.0494
Abstract：Bundling recommendation enhances user experienceand boosts merchant sales performance byofering predefined setsof productcombinations.It also playsa significantrole in various serviceecosystems suchas video-on-demandandmusic playlistgeneration.Existingbundlingrecommendationmethodsoftenrelyonsharedmodel parametersormulti-task learning schemes，neglectingthedeep-levelconnectionsamongusers，items，andbundles，hichleadstoinformationlossandipacts the performanceofrecommendationsystems.Toadesstheseissues，tispaper proposedacompletehypergraphneuralnetwork （CHNN）.Firstly，theframeworkconstructedacompletehypergraphtoexpressthetemaryelationshipsamong users，items，nd bundles.Theseternaryrelationshipsnotonlyincludedtheinterconnectionsamongusers，items，andbundled，butalsoecompasedthe interalconnectionswithinusersandbundles，effectivelydescribingtherelationshipbetweenproductbundlesand userpreferences.Scondly，temodelconsistsofaninitializationlayer，atripleconvolutionlayer，andapredictionlayer.The initializationlayergeneratedembeddingvectorsforeachuser，item，andbundle.Thetripleconvolutionlayerextractediforationfromthecompletehypergraphandleveragedtheuser-bundle graphand item-bundlegraph toenhancetherepresentations of users，items，andbundles.The predictionlayer providedrecommendationsbasedonthefinalembedding vectors.Through multi-layerrichconvolutionoperations，themodel fullyexploredtheassciationscontained inthecomplete hypergraphto achieve moreaccuraterecommendations.Experimentson tworeal-worlddatasets，NetEaseandYouShu，demonstratethat CHNN achieves an average improvement of 2.4% in recall and 2.75% in NDCG，outperforming existing baseline models and showcasing its effectiveness in the field of bundling recommendation.
Key words：graph neural network；bundle recommendation；hyper graph；graph convolutional network
0 引言
隨著營銷策略的快速發(fā)展，商家已經(jīng)考慮向用戶推薦一組商品[1-5]。（剩余19197字）