568 citations · 6.2k across the 157 of their papers we have counts for
25 papers · 1 filter
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Haokun Chen, Xinyi Dai, Han Cai +5
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-…
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
Haifeng Zhang, Zilong Guo, Han Cai +5
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume. Fo…
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Zhiming Zhou, Qingru Zhang, Guansong Lu +3
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, bu…
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
Liheng Chen, Yanru Qu, Zhenghui Wang +5
In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding model…
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Ming Zhou, Yong Chen, Ying Wen +5
Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to…
Learning Adaptive Display Exposure for Real-Time Advertising
Weixun Wang, Junqi Jin, Jianye Hao +9
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. Howeve…
Deep Recurrent Survival Analysis
Kan Ren, Jiarui Qin, Lei Zheng +4
Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as…
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
Kan Ren, Yuchen Fang, Weinan Zhang +5
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to a…
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Ruijie Wang, Yuchen Yan, Jialu Wang +4
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale…
On the Equilibrium of Query Reformulation and Document Retrieval
Shihao Zou, Guanyu Tao, Jun Wang +2
In this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelled as a…
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
Zhiming Zhou, Yuxuan Song, Lantao Yu +5
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs. We study the property of the optimal discriminative function and show…
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
Yanru Qu, Bohui Fang, Weinan Zhang +5
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response…
Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
Weinan Zhang
Generative adversarial nets (GANs) have been widely studied during the recent development of deep learning and unsupervised learning. With an adversarial training mechanism, GAN ma…
Path-Level Network Transformation for Efficient Architecture Search
Han Cai, Jiacheng Yang, Weinan Zhang +2
We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing…
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Zhenghui Wang, Yanru Qu, Liheng Chen +7
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical…
A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks
Huichen Li, Xiaojun Xu, Chang Liu +6
Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents…
CoT: Cooperative Training for Generative Modeling of Discrete Data
Sidi Lu, Lantao Yu, Siyuan Feng +3
In this paper, we study the generative models of sequential discrete data. To tackle the exposure bias problem inherent in maximum likelihood estimation (MLE), generative adversari…
QA4IE: A Question Answering based Framework for Information Extraction
Lin Qiu, Hao Zhou, Yanru Qu +7
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open…
Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning
Li He, Liang Wang, Kaipeng Liu +2
Sponsored search is an indispensable business model and a major revenue contributor of almost all the search engines. From the advertisers' side, participating in ranking the searc…
Neural Text Generation: Past, Present and Beyond
Sidi Lu, Yaoming Zhu, Weinan Zhang +2
This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the tradit…
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
Kan Ren, Weinan Zhang, Ke Chang +3
Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impres…
Collaborative Filtering with Graph-based Implicit Feedback
Minzhe Niu, Weinan Zhang, Yanru Qu +4
Introducing consumed items as users' implicit feedback in matrix factorization (MF) method, SVD++ is one of the most effective collaborative filtering methods for personalized reco…
Texygen: A Benchmarking Platform for Text Generation Models
Yaoming Zhu, Sidi Lu, Lei Zheng +4
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but…
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Junqi Jin, Chengru Song, Han Li +3
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by…