基于深度学习的电商商品购买意图识别模型  

Purchasing Intention Identification Model Based onDeep Learning in E-commerce

作　　者：郭小宇 马静[1] GUO Xiaoyu;MA Jing(College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学经济与管理学院,江苏南京211106

出　　处：《运筹与管理》2024年第1期145-150,共6页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(72174086);中央高校基本科研业务费专项资金项目(NW2020001)。

摘　　要：识别用户的购买意图是提升电子商务购买率(PR)的重要方法之一。针对用户购买意图不明确的现象,提出一种新模型。该模型将训练后的Word2Vec(WV)词向量馈入卷积神经网络(CNN),通过深层语义模型(DSSM)进一步提取文本特征。在Keras框架下结合美国建材电商网站家得宝的真实搜索数据进行实证分析。结果表明,在五分类问题中,新模型在测试数据集上的F1-score达80.6%。新模型使用了Word2Vec与CNN提取文本特征,并应用DSSM模型进一步提取了用户检索与商品描述文档在高维空间中的特征表示,最大化利用了用户检索与正确商品描述之间的语义相似度,同时避免了特征提取时主观因素的干扰,提高了商品购买意图的识别效果。With the rapid proliferation and intelligent development of e-commerce platforms,accurate identification of user purchase intention has become a crucial influencing factor in driving users from intent to actual purchases.Therefore,identifying user purchase intention is one of the significant methods to enhance the Purchase Rate(PR)in the realm of e-commerce.Purchase intention identification aims to infer the intended purchase of potential customers or users by analyzing the similarity between user query text and product description text,ultimately increasing the PR.Due to the diversity and colloquial nature of user search queries,identifying user purchase intention becomes increasingly challenging,and even more so in vertical e-commerce where users may not even be aware of the names of the products they need.In response to the phenomenon of unclear user purchase intention,this paper proposes a novel model aimed at identifying user purchase intention from user queries with unclear purchase intention.This model first employs the Word2Vec(WV)algorithm’s Continuous Bag-of-Words(CBOW)model to train word vectors.Subsequently,these word vectors are fed into a one-dimensional Convolutional Neural Network(CNN),followed by further feature extraction using the Deep Semantic Similarity Model(DSSM).This process calculates semantic similarity using cosine similarity,subsequently transforming semantic similarity into a posterior probability form to construct a loss function.During model training,it narrows the textual representations in a high-dimensional space between user queries and intended products while expanding the representations between user queries and non-intended products.An empirical analysis is conducted using real search data from the U.S.building materials e-commerce website Home Depot,within the Keras framework.The results indicate that our proposed model achieves an F1-score of 80.6%on the test dataset in a five-class classification problem.To test the performance of the model proposed in this paper in more

关 键 词：购买意图识别 卷积神经网络 深层语义模型 深度学习 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]