52 citations · 255 across the 25 of their papers we have counts for
25 papers
Recurrent Temporal Revision Graph Networks
Yizhou Chen, Anxiang Zeng, Guangda Huzhang +6
Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for tempor…
Disruptive papers in science are losing impact
An Zeng, Ying Fan, Zengru Di +2
The impact and originality are two critical dimensions for evaluating scientific publications, measured by citation and disruption metrics respectively. Despite the extensive effor…
Clustered Embedding Learning for Recommender Systems
Yizhou Chen, Guangda Huzhang, Anxiang Zeng +7
In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector f…
Predicting the cascading dynamics in complex networks via the bimodal failure size distribution
Chongxin Zhong, Yanmeng Xing, Ying Fan +1
Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors.…
Impactful scientists have higher tendency to involve collaborators in new topics
An Zeng, Ying Fan, Zengru Di +2
In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, resources, and for performing interdisciplinary research. Although c…
Academic mentees succeed in big groups, but thrive in small groups
Yanmeng Xing, Ying Fan, Roberta Sinatra +1
Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. A common success metric in the scientific enter…
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Danni Peng, Sinno Jialin Pan, Jie Zhang +1
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the…
Diversity Regularized Interests Modeling for Recommender Systems
Junmei Hao, Jingcheng Shi, Qing Da +4
With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult…
Hybrid Interest Modeling for Long-tailed Users
Lifang Deng, Jin Niu, Angulia Yang +4
User behavior modeling is a key technique for recommender systems. However, most methods focus on head users with large-scale interactions and hence suffer from data sparsity issue…
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
Yuting Chen, Yanshi Wang, Yabo Ni +2
Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users…
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
Yanshi Wang, Jie Zhang, Qing Da +1
Estimating post-click conversion rate (CVR) accurately is crucial in E-commerce. However, CVR prediction usually suffers from three major challenges in practice: i) data sparsity:…
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
Jianxiong Wei, Anxiang Zeng, Yueqiu Wu +3
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either d…
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao +8
Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collecte…
Matrix embedding method in match for session-based recommendation
Qizhi Zhang, Yi Lin, Kangle Wu +2
Session based model is widely used in recommend system. It use the user click sequence as input of a Recurrent Neural Network (RNN), and get the output of the RNN network as the ve…
Policy Optimization with Model-based Explorations
Feiyang Pan, Qingpeng Cai, An-Xiang Zeng +5
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games…
Selective Zero-Shot Classification with Augmented Attributes
Jie Song, Chengchao Shen, Jie Lei +4
In this paper, we introduce a selective zero-shot classification problem: how can the classifier avoid making dubious predictions? Existing attribute-based zero-shot classification…
Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design
Hua-Lin He, Chun-Xiang Pan, Qing Da +1
In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform. Existing methods mainly focus on the short-term return…
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Yabo Ni, Dan Ou, Shichen Liu +4
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent u…
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Jing-Cheng Shi, Yang Yu, Qing Da +2
Applying reinforcement learning in physical-world tasks is extremely challenging. It is commonly infeasible to sample a large number of trials, as required by current reinforcement…
Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke +3
Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling…
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection
Yusen Zhan, Qing Da, Fei Xiao +2
In industrial large-scale search systems, such as Taobao.com search for commodities, the quality of the ranking result is getting continually improved by introducing more factors f…
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
Yujing Hu, Qing Da, Anxiang Zeng +2
In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widel…
Enhancing synchronization in growing networks
Y. Wang, A. Zeng, Z. Di +1
Most real systems are growing. In order to model the evolution of real systems, many growing network models have been proposed to reproduce some specific topology properties. As th…
Scaling properties in spatial networks and its effects on topology and traffic dynamics
Hua Yang, Yuchao Nie, Ying Fan +2
Empirical studies on the spatial structures in several real transport networks reveal that the distance distribution in these networks obeys power law. To discuss the influence of…