基于AIGC的用户个性化需求服务匹配模型研究

Service Matching Model of Users' Personalized Demands Based on AIGC

中文关键词: 用户需求匹配  AIGC应用  点对点匹配  扎根理论  深度学习

英文关键词: user's demand matching  application of AIGC  Point-to-Point Matching  rooted theory  deep learning

中文摘要:

      目的 为解决网购推送内容相关度较低、信息滞后而造成的用户疲劳等问题，优化推送系统的个性化推荐能力，对AIGC推荐算法下的交互模式进行可行性探索。方法 基于扎根理论获取用户关联属性并对其排序，进而利用AIGC大模型分别对用户需求信息和产品特征图文进行处理，最终建立基于AIGC的“用户-产品点对点匹配”(User-Product Point-To-Point Matching)推送模型。结果 以水杯网购过程为例，通过对比实验进行验证。实验结果表明，该模式在用户选择信息层和深度搜索等方面可提升用户满意度。结论 将AIGC引入推送服务行业，通过“预训练语言模型+微调”的模式实现推荐，能够进一步完善“用户-产品”的个性化推送服务，在电子商务、社交媒体、在线教育服务等领域具有重要意义。

英文摘要:

      In order to solve the problems of user fatigue caused by low relevance of push content and lagging information in online shopping, the work aims to optimize the personalized recommendation ability of the push system, and explore the feasibility of the interaction mode under the AIGC recommendation algorithm. Based on the rooted theory, the user association attributes were obtained and sorted, and then the users' demand information and product feature images were processed by the AIGC large model. Finally, the push model of "User-Product Point-To-Point Matching" based on AIGC was established. With the online shopping process of water cup as an example, the comparison experiment was carried out for verification. The experimental results showed that this model could improve user satisfaction in the aspects of information selection layer and deep search. The introduction of AIGC into the push service industry and the implementation of recommendation through the mode of "pre-training language model + fine-tuning" can further improve the personalized push service of "user-product", which is of great significance in e-commerce, social media, online education services and other fields.