面向知识图谱的网络信息自监督强化学习推荐模型

打开文本图片集

引用格式：.面向知识图谱的网络信息自监督强化学习推荐模型[J].现代电子技术，2025，48（10）：142-146

关键词：知识图谱；网络信息；自监督；强化学习；推荐模型；交互信息；特征提取；相似度计算中图分类号：TN711-34；TP182 文献标识码：A 文章编号：1004-373X（2025）10-0142-05

Abstract：Inorder todeeplyunderstandandmine thebehavioralcharacteristicsof user historicalnetworkinteraction information，dynamicallextractchangesinuserinteractionbehaviorandachievepersonalizedrecommendationofnetwork information，aknowledgegraph basednetwork informationself-supervisedreinforcementlearningrecommendationmodelis constructed.Inthemodel，aknowledgegraphofusernetworkinformationinteractionbehaviorisonstructedtoclearlydisplay user'shistoricalnetwork information interactionbehavior.Thedynamicchangesofuserbehaviorintheknowledgegraphcanbe capturedefectivelybymeansofthefeatureextractionmodelbasedonself-supervisedreinforcement learningtoavoidthe negativeimpactofpopularityias，soastoextractthefeaturesof historicalnetworkinteractioninformation.Basedonknowledge graphsimilaritycalculation，thenetworkinformationentitieswithsimilarfeatures touserhistorical interaction informationare recommendedtorealizetheaccurateandpersonalizedrecommendations.Theexperimentalresultsverifiesthatafter recommending online movie information resources to users，the click play conversion rate can reach 96.83% ，and the personalized recommendation effect of online information is improved significantly.

Keywords：knowledgegraph；network information；self supervision；reinforcement learning；recommendationmodel; interactive information; feature extraction; similarity calculation

0 引言

随着社交媒体、电子商务和在线视频等领域的繁荣发展，网络信息呈现出多样化、海量化和快速更新的特点，这使得用户难以从海量信息中筛选出符合自己兴趣和需求的内容。（剩余5834字）