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
白雪 1王霞光 1金继鑫 2宋春梅 2赵思彤3
作者信息
- 1. 中国科学院沈阳计算技术研究所,沈阳 110168;中国科学院大学,北京 100049
- 2. 中国科学院沈阳计算技术研究所,沈阳 110168
- 3. 沈阳工业大学,沈阳 110870
- 折叠
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.
Abstract
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 e-commerce 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Key words
agricultural product recommendation/click through rate(CTR)prediction/feature interaction/feature selection/deep neural network(DNN)引用本文复制引用
基金项目
辽宁省应用基础研究计划(2022JH2/101300126)
出版年
2024
NETL
NSTL
万方数据