基于细粒度特征交互选择网络的农产品推荐算法  

Agricultural Product Recommendation Algorithm Based on Fine-grained Feature Interactive Selection Network

作　　者：白雪 王霞光 金继鑫 宋春梅 赵思彤 BAI Xue;WANG Xia-Guang;JIN Ji-Xin;SONG Chun-Mei;ZHAO Si-Tong(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]中国科学院沈阳计算技术研究所,沈阳110168 [2]中国科学院大学,北京100049 [3]沈阳工业大学,沈阳110870

出　　处：《计算机系统应用》2024年第5期271-279,共9页Computer Systems & Applications

基　　金：辽宁省应用基础研究计划(2022JH2/101300126)。

摘　　要：在数字化的时代里,越来越多人偏爱在电商平台购物,随着农产品电商平台的发展,消费者面对众多选择时难以找到适合自己的产品.为了提高用户满意度和购买意愿,农产品电商平台需要根据用户的兴趣偏好向其推荐合适的农产品.考虑到季节、地域、用户兴趣和农产品属性等多种农业特征,通过特征交互可以更好地捕捉用户需求.传统的点击通过率CTR (click through rate)预测模型只关注用户评分,以简单的方式计算特征交互,而忽略了特征交互的重要性.本文提出了一种名为细粒度特征交互选择网络FgFisNet (fine-grained feature interaction selection networks)的新模型.该模型通过引入细粒度交互层和特征交互选择层,组合内积和哈达玛积有效地学习特征交互,然后在训练过程中自动识别重要的特征交互,并删除冗余的特征交互,最后将重要的特征交互和一阶特征输入到深度神经网络,得到最终的CTR预测值.在农产品电商真实数据集上进行广泛的实验,FgFisNet方法取得了显著的经济效益.In the digital era,an increasing number of people prefer shopping on e-commerce platforms.With the development of agricultural product e-commerce platforms,consumers find it challenging to discover suitable products among numerous choices.To enhance user satisfaction and purchase intent,agricultural product e-commerce platforms need to recommend appropriate products based on user preferences.Considering various agricultural features such as season,region,user interests,and product attributes,feature interactions can better capture user demands.This study introduces a new model,fine-grained feature interaction selection networks(FgFisNet).The model effectively learns feature interactions using both the inner product and Hadamard product by introducing fine-grained interaction layers and feature interaction selection layers.During the training process,it automatically identifies important feature interactions,eliminates redundant ones,and feeds the significant feature interactions and first-order features into a deep neural network to obtain the final click through rate(CTR) prediction.Extensive experiments on a real dataset from agricultural ecommerce demonstrate significant economic benefits achieved by the proposed FgFisNet method.

关 键 词：农产品推荐 点击率预测 特征交互 特征选择 深度神经网络 

分 类 号：F323.7[经济管理—产业经济] F724.6[自动化与计算机技术—计算机应用技术] TP391.3[自动化与计算机技术—计算机科学与技术]