面向海量數(shù)據(jù)場景的生成對抗網(wǎng)絡(luò)推薦算法

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引用格式：，，.面向海量數(shù)據(jù)場景的生成對抗網(wǎng)絡(luò)推薦算法[J].現(xiàn)代電子技術(shù)，2025，48（10）：71-75.

關(guān)鍵詞：海量數(shù)據(jù)場景；生成對抗網(wǎng)絡(luò)；長短期記憶網(wǎng)絡(luò)；推薦算法；動態(tài)序列；個性化推薦；目標函數(shù)中圖分類號：TN919.2-34；TP391 文獻標識碼：A 文章編號：1004-373X（2025）10-0071-05

Abstract：Masivedataoftencontainscomplexuserbehaviorpatterns，itematributesandtheirrelationships，whichoften havenon-linearcharacteristics.Traditional generativeadversarial network （GAN）mayfacechallngesinnonlinear modeling whenprocesingsequencedata.Inordertoefectivelycapturethelong-shortterminterestchangesofusers，enrichthediversity ofcontent，enhancetheprocessingabilityandstabilityinmassivedatascenarios，agenerativeadversarialnetwork recommendtionalgorithmformassivedatascenariosisproposed.Inthelong-shorttermmemorynetwork（LSTM），theuser's behaviorpaterns towardsthedatasceneareusedasinputtooutput the dynamicsequenceof long-shorttermdatascenesof interesttotheuser.TheLSTMiscombinedwithGANtoformanL-GANrecommendationmodel.Inthismodel，thelong-short termdynamicsequencesoutputbyLSTMareinputintothegeneratorofGAN，andfalsesamplessimilartorealdatascenarios aregeneratedbyoptimizingthelossfunction.Thefakesamplesareinputintothediscriminatortogetherwiththerealdata scenes，andtheauthenticityisidentifiedbymeansofitsobjectivefunction.Afterrepeatedcompetitionandtraining，thegeerator anddiscriminatorcanformanaccuraterecommendationnetwork，soastofinallyoutputarecommendationlistofdatascenesthat meetthe user's interests.Theexperimental resultsshow that the proposed algorithmcan acuratelycapture the nedsof users when processing massive data scenes，and make eficient and comprehensive personalized recommendations.

Keywords：masivedatascenario;generative adversarial network；long-short term memory network；recommendation algorithm;dynamic sequence;personalized recommendation;objective function

0 引言

面向海量數(shù)據(jù)場景的推薦算法能夠幫助企業(yè)和平臺更精準地理解用戶需求，為產(chǎn)品研發(fā)和決策提供有力支持。（剩余5746字）