Learning Hierarchical Category Influence on both Users and Items for Effective Recommendation 2020-04-15 11:01:03
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...

Modeling Buying Motives for Personalized Product Bundle Recommendation 2019-12-13 20:40:23
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...

Multiple Relational Attention Network for Multi-task Learning 2019-12-12 10:10:52
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...

Temporal Relational Ranking for Stock Prediction 2019-12-06 21:44:54
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...

SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS 2019-12-05 15:54:05
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...

Adversarial Substructured Representation Learning for Mobile User Profiling 2019-12-03 19:55:49
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...

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems 2019-12-02 11:32:36
Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for develo... ...

Identifying Challenges and Opportunities in Human–AI Collaboration in Healthcare 2019-12-02 10:14:39
The proposed workshop will identify research questions that will enable the field to uncover the types of work, labor relations, and social impacts that should be considered when designing AIbased healthcare technology. The workshop aims to outline key challenges, guidelines, and future agendas for ... || CSCW; Healthcare...

Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System 2019-11-29 10:57:55
Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and l... || Transportation recommendation; context-aware; personalized; feature engineering; deployment; POI 特征工程; || Hao Liu; Hui Xiong...

Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network 2019-11-28 09:39:11
Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the follo... || Demand Prediction, Spatio-Temporal Analysis, Sharing Economy, Deep Neural Network; || Junchen Ye; Hui Xiong...