Grounded Study of Internet Addiction Behavior in the Context of Mobile Intelligent Recommendation

AbstractAI-driven recommendation algorithms have been widely deployedacross mobilesocial media platforms， enablingpersonalized informationdeliverythrough learning fromusers'historical behaviorsand interestpreferences. Theresultingmobile inteligentrecommendationecosystemunderscoresthedisruptiveapplicationsofAIintherecommendation domain.While mobile inteligentrecommendationsbettersatisfyusers'information needs，theyalso exacerbateaddictiveonlinebehaviors.This study employs grounded theory to explore theunderlying mechanisms influencing userinternetaddictionwithinmobileinteligentrecommendationcontexts.First，aninterviewprotocolwasdesigned，nd data werecollected through semi-structured interviews.Three-tiercoding Was thenconducted： during open coding，l32 initialconceptsand33basiccategories wereextracted;throughaxialcoding，lOprincipalcategories wereidentified;and duringselectivecoding，atheoreticalmodelofinternetaddictionmechanismsinmobileinteligentrecommendationscenarios wasconstructed.Tefindingsrevealthatpersonaliteracyehavioralbeliefs，exteralifluences，egativemotions， andreal-worldconditions exertdirectimpactsoninternetaddictionbehaviors.Meanwhile，theinformationqualitysystem quality，andservicequalityofintellgentrecommendations indirectlyinfluencebehavioralbeliefs throughmediatingeffectsof usersatisfaction，therebyaectingaddictivebehaviors.Thisresearch，toacertainextent，elucidates thesocietal impactsofAI'sdisruptiveapplicationsinrecommendationsystems，providing theoreticalreferencesforinterventionsand governance strategies addressing user internet addiction within mobile intelligent recommendation environments.

/wordsmobilesocialmedia;inteligent recommendation;disruptive application;internetaddiction; grounded th

随着人工智能（ArtificialIntelligence，AI）技术的快速发展和移动社交媒体的广泛使用，算法驱动的智能推荐系统已深度嵌入移动社交媒体平台，对人们获取信息的方式产生了颠覆性影响[1]。传统的信息获取依赖于用户主动检索和筛选，而AI驱动的智能推荐则通过对用户历史行为和兴趣偏好的深度学习，实现高度个性化与自动化的信息推送[2。这一变革打破了信息单向流动的被动局面，使用户在移动社交媒体环境下逐步从主动搜寻转向被动接收，用户在不知不觉中被引导进入高度定制化的信息环境。这不仅重构了个人的信息边界和认知反馈回路，也在社会层面推动了知识分布、舆论生成和社交结构的深刻变革，形成了具有鲜明颠覆性特征的移动智能推荐情境[3]。

然而，智能推荐在带来便利和高效的同时，也引发了新的社会行为问题。其高度个性化和持续推送机制容易导致用户产生过度依赖，诱发网络成瘾行为。网络成瘾表现为个体因长时间、持续性地沉迷于平台内容，出现的注意力分散、时间管理失控、社交退缩及身心健康受损等负面表现[4-5]。移动智能推荐情境凸显了人工智能在推荐领域的颠覆性应用，本研究系统探究了移动智能推荐情境下用户网络成瘾行为的影响机理，为移动智能推荐情境下网络成瘾行为的干预与治理提供理论参考。

1文献综述

1.1人工智能在推荐领域的颠覆性应用

人工智能技术已深度融入移动互联网用户的日常生活。……