基于细粒度特征交互选择网络的农产品推荐算法

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.

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

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.