基于超圖和分層頻譜濾波器的序列推薦模型
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中圖分類號：TP391.3 文獻標(biāo)志碼：A 文章編號：1001-3695（2025）07-013-2018-07
doi： 10.19734/j. issn.1001-3695.2024. 12.0488
Abstract：Toaddress thedata sparsity problem prevalent insequence recommendationandthe noise isue caused byunexpected interactions between users and items，this paper proposed asequence recommendation model basedon hypergraph and hierarchical spectralflters（HYFTRec）：HYFTRec introducedthe hypergraph structure intosequence recommendation， captured thecomplex higher-orderrelationshipsbetween usersanditems through thehypergraphembedding module，and atthe sametimeutilizedthehierarchicalspectralfilterforeffctivedenoisinginthefrequencydomainforefectivedenoising，which improved theaccuracyandrobustnessofrecommendation.Inaddition，themodel incorporatedacomparative learning framework tooptimize the characterizationabilityof userbehaviorsequences.Through multipleexperimentalvalidationsonthree public datasets，HYFTRecdemonstratesadvantages intermsof keymetricshitrate（HR）and normalizeddiscountcumulative gain（NDCG），which significantlyoutperformsexisting sequencerecommendation models.Compared with the benchmark modelFMLP，HYFTRec improves 10.7% ， 10.8% ，and 7.6% in HR@ 10 metrics and 12.5% ， 13.7% ，and 7.3% in NDCG@10 metrics，respectively.These results verify the validity and superiority of the proposed model.
Key words：hypergraph；hierarchical spectral filter；contrast learning；sequence recommendation
0 引言
近年來，隨著推薦系統(tǒng)的廣泛應(yīng)用，序列推薦逐漸成為研究的熱點領(lǐng)域[1＼～4]。（剩余17639字）