E-commerce Search via Content Collaborative Graph Neural Network

计算机科学 可扩展性 图形 注意力网络 反事实思维 机器学习 人工神经网络 人工智能 理论计算机科学 数据挖掘 情报检索 数据库 认识论 哲学
作者
Guipeng Xv,Chen Lin,Wanxian Guan,Jinping Gou,Xubin Li,Hongbo Deng,Jian Xu,Bo Zheng
标识
DOI:10.1145/3580305.3599320
摘要

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs). Despite their promising performances, they are (1) lacking proper semantic representation of product contents; (2) less efficient for industry-scale graphs; and (3) less accurate on long-tail queries and cold-start products. To address these problems simultaneously, this paper proposes CC-GNN, a novel Content Collaborative Graph Neural Network. Firstly, CC-GNN enables content phrases to participate explicitly in graph propagation to capture the proper meaning of phrases and semantic drifts. Secondly, CC-GNN presents several efforts towards a more scalable graph learning framework, including efficient graph construction, MetaPath-guided Message Passing, and Difficulty-aware Representation Perturbation for graph contrastive learning. Furthermore, CC-GNN adopts Counterfactual Data Supplement at both supervised and contrastive learning to resolve the long-tail/cold-start problems. Extensive experiments on a real E-commerce dataset of 100-million-scale nodes show that CC-GNN produces significant improvements over existing methods (i.e., more than 10% improvements in terms of several key evaluation metrics for overall, long-tail queries and cold-start products) while reducing computational complexity. The proposed components of CC-GNN can be applied to other models for search and recommendation tasks. Experiments on a public dataset show that applying the proposed components can improve the performance of different recommendation models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
香蕉觅云应助科研通管家采纳,获得10
1秒前
小马甲应助科研通管家采纳,获得10
1秒前
充电宝应助科研通管家采纳,获得10
1秒前
1秒前
1秒前
顾矜应助科研通管家采纳,获得10
2秒前
JamesPei应助科研通管家采纳,获得10
2秒前
隐形曼青应助科研通管家采纳,获得10
2秒前
英姑应助科研通管家采纳,获得10
2秒前
充电宝应助科研通管家采纳,获得10
2秒前
Maestro_S应助科研通管家采纳,获得10
2秒前
wanci应助科研通管家采纳,获得10
2秒前
Owen应助科研通管家采纳,获得10
2秒前
斯文败类应助科研通管家采纳,获得30
2秒前
2秒前
高高亿先应助科研通管家采纳,获得10
2秒前
烟花应助科研通管家采纳,获得10
2秒前
2秒前
ding应助科研通管家采纳,获得10
2秒前
1sunpf完成签到,获得积分10
2秒前
2秒前
2秒前
2秒前
脑洞疼应助科研通管家采纳,获得10
2秒前
spf完成签到,获得积分10
3秒前
荒野风发布了新的文献求助10
3秒前
luxkex完成签到,获得积分10
3秒前
3秒前
奶黄包发布了新的文献求助10
3秒前
有求必_应完成签到,获得积分10
4秒前
5秒前
ShuY完成签到,获得积分10
5秒前
careyzhou发布了新的文献求助10
5秒前
Ran-HT完成签到,获得积分10
6秒前
开小森发布了新的文献求助10
7秒前
科研通AI2S应助荒野风采纳,获得10
7秒前
闫木木完成签到,获得积分10
8秒前
邪恶青年完成签到,获得积分10
8秒前
xuan完成签到,获得积分10
8秒前
yahonyoyoyo发布了新的文献求助10
9秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038426
求助须知:如何正确求助?哪些是违规求助? 3576119
关于积分的说明 11374556
捐赠科研通 3305834
什么是DOI,文献DOI怎么找? 1819339
邀请新用户注册赠送积分活动 892678
科研通“疑难数据库(出版商)”最低求助积分说明 815029