Research on recommendation of

基于多维用户画像和DeepFM的“环评云助手”资源推荐研究.pdf

摘要(Abstract)：

“环评云助手”是一款服务于环境影响评价行业用户的APP,针对APP中信息量激增、行业资源文本特征利用不充分和行业用户即时资源推荐精准较低等问题，提出一种结合行业文本资源和用户行为特征的多维用户画像模型并应用于深度因子分解机(Deep Factorization Machines, DeepFM),实现资源点击率(Click-Through-Rate, CTR)的精准预测.模型首先对行业资源文本进行语义抽取，再对行业用户行为进行自定义评分，从而构建多维用户画像模型；最后将多维用户画像应用于DeepFM模型，进行CTR预测任务，实现具有行业特征的个性化推荐.实验数据来自“环评云助手”APP,实验结果表明该模型有效提高了CTR预测任务的AUC值，降低了LogLoss值，具有一定的应用价值.
EIA Cloud is an APP that serves users in the Environmental Impact Assessment. In view of the problems such as the surge of information, the insufficient use of text features and the low accuracy of real-time resource recommendation by users, the paper proposes a multi-dimensional user portrait model based on DeepFM combined with industry resources and user behavior to achieve CTR prediction. Firstly, the industry resource semantics is extracted, and then the user behavior is scored to build a multi-dimensional user portrait model. Finally, the model is applied to DeepFM to perform CTR prediction and achieve personalized recommendation with industry feature. Experimental data are obtained from EIA Cloud. The experimental results show that the model can effectively improve the AUC value of CTR prediction tasks and reduce the LogLoss value, which has certain application value.

关键词(KeyWords)：环境影响评价;用户画像;标签生成;推荐算法;深度因子分解机;CTR预测任务
Environmental Impact Assessment;user portrait;tag generation;recommendation algorithm;DeepFM;CTR prediction

基金项目(Foundation):国家自然科学基金(51975058);; 教育部人文社科规划基金(20YJAZH129);; 2022年北京信息科技大学优质课程专项;; 北京信息科技大学2023课程思政立项项目(2023JGSZ20)

作者(Authors):李天玉;车蕾;丁峰;谭悦;
Li Tianyu;Che Lei;Ding Feng;Tan Yue;School of Information Management, Beijing Information Science and Technology University;Beijing Shangyun Co., LTD.;

DOI:10.16366/j.cnki.1000-2367.2023.04.006

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