面向海量数据场景的生成对抗网络推荐算法

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引用格式：，，.面向海量数据场景的生成对抗网络推荐算法[J].现代电子技术，2025，48（10）：71-75.

关键词：海量数据场景；生成对抗网络；长短期记忆网络；推荐算法；动态序列；个性化推荐；目标函数中图分类号：TN919.2-34；TP391 文献标识码：A 文章编号：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

面向海量数据场景的推荐算法能够帮助企业和平台更精准地理解用户需求，为产品研发和决策提供有力支持。（剩余5746字）