Zobrazeno 1 - 10
of 11
pro vyhledávání: '"ZHAO LI"'
Publikováno v:
WWW
A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label colu
Publikováno v:
CIKM
Recommending stock with the highest return ratio is always a challenging problem in the field of financial technology. In this paper, we propose a time-aware graph relational attention network (TRAN) for stock recommendation based on return ratio ran
Publikováno v:
CIKM
Large-scale online promotions, such as Double 11 and Black Friday, are of great value to e-commerce platforms nowadays. Traditional methods are not successful when we aim to maximize global Gross Merchandise Volume (GMV) in the promotion scenarios du
Autor:
Pengcheng Zou, Zhao Li, Rong Xiao, Xiaoru Qu, Jun Gao, Jiaming Huang, Jialin Wang, Ji Zhang, Zhipeng Zhang, Junxiao Jiang
Publikováno v:
CIKM
Helpful reviews in e-commerce sites can help customers acquire detailed information about a certain item, thus affecting customers' buying decisions. Predicting review helpfulness automatically in Taobao is an essential but challenging task for two r
Publikováno v:
CIKM
The e-commerce era is witnessing a rapid development of various annual online promotions, such as Black Friday, Cyber Monday, and Alibaba's 11.11, etc. S ales P redictions for O nline P romotions (SPOP) are a set of sales related forecasts for the pr
Publikováno v:
DSIT
In order to improve the accuracy of fault diagnosis of power transformer in the presence of missing values, we propose a missing value imputation method by iterative k nearest neighbor (KNN), and ensemble learning is used to diagnose the power transf
Publikováno v:
WWW
Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles bo
Publikováno v:
WWW
Modeling user behaviors as sequences provides critical advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for personalized search and recommendation. Recently, recurrent neural
Publikováno v:
WWW
Fraud transactions are one of the major threats faced by online e-commerce platforms. Recently, deep learning based classifiers have been deployed to detect fraud transactions. Inspired by findings on adversarial examples, this paper is the first to
Publikováno v:
KDD
Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to mea