Item category has proven to be useful additional informa- tion to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many real applications, item category is often organi... || category information; || Zhu Sun; Jie Zhang...
Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. T... || 主题模型；生成模型；解释；利用物品特征和集合关系构造物品之间的联系；构图模式; || HUI XIONG...
Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task r... || attention model; 多任务学习; || Fuzhen Zhuang; Hui Xiong...
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has becom... || Stock Prediction, Learning to Rank, Graph-based Learning, GCN； 股票收益预测; || FULI FENG, XIANGNAN HE...
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral... || GCN; 图卷积简化过程; || Thomas N. Kipf; Max Welling...
Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user’s interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via repre... || Mobile User Profiling, Substructure, Representation Learning; 生成对抗网络；; || Yanjie Fu; Hui Xiong...
