Search Results for author: Xingxing Wang

Found 24 papers, 4 papers with code

Off-Policy Primal-Dual Safe Reinforcement Learning

2 code implementations26 Jan 2024 Zifan Wu, Bo Tang, Qian Lin, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang

Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training.

reinforcement-learning Safe Reinforcement Learning

Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed

no code implementations3 Jan 2024 Xuejian Li, Ze Wang, Bingqi Zhu, Fei He, Yongkang Wang, Xingxing Wang

The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement.


HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning

no code implementations29 Dec 2023 Hao Wang, Bo Tang, Chi Harold Liu, Shangqin Mao, Jiahong Zhou, Zipeng Dai, Yaqi Sun, Qianlong Xie, Xingxing Wang, Dong Wang

Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day.

Data Augmentation

RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems

no code implementations27 Dec 2023 Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang, Qianlong Xie, Lebin Lin, Xingxing Wang, Dong Wang

The existing studies focus on dynamically allocating CRs in queue truncation scenarios (i. e., allocating the size of candidates), and formulate the CR allocation problem as an optimization problem with constraints.

Model Selection Recommendation Systems +1

ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation

no code implementations28 Nov 2023 Junyan Qiu, Haitao Wang, Zhaolin Hong, Yiping Yang, Qiang Liu, Xingxing Wang

The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and transferable recommendations.

Contrastive Learning Language Modelling +1

SNR-Independent Joint Source-Channel Coding for wireless image transmission

no code implementations27 Jun 2023 Hongjie Yuan, Weizhang Xu, Yuhuan Wang, Xingxing Wang

Our experiments have shown that SIJSCC outperforms existing channel adaptive DL-based JSCC methods that rely on SNR information.

A Collaborative Transfer Learning Framework for Cross-domain Recommendation

no code implementations26 Jun 2023 Wei zhang, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang

The disadvantage of the former is that the data from other domains is not utilized by a single domain model, while the latter leverage all the data from different domains, but the fine-tuned model of transfer learning may trap the model in a local optimum of the source domain, making it difficult to fit the target domain.

Click-Through Rate Prediction Recommendation Systems +1

Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction

no code implementations5 Jun 2023 Huinan Sun, Guangliang Yu, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang

It consists of a multi-interest graph structure for capturing long-term user behavior, a multi-scenario heterogeneous sequence model for modeling short-term information, then an adaptive fusion mechanism to fused information from long-term and short-term behaviors.

Click-Through Rate Prediction

Safe Offline Reinforcement Learning with Real-Time Budget Constraints

1 code implementation1 Jun 2023 Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang

Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years.

reinforcement-learning Reinforcement Learning (RL)

MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed

no code implementations17 Apr 2023 Xiaowen Shi, Ze Wang, Yuanying Cai, Xiaoxu Wu, Fan Yang, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang

There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data.

Imitation Learning Position +2

PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

1 code implementation6 Feb 2023 Xiaowen Shi, Fan Yang, Ze Wang, Xiaoxu Wu, Muzhi Guan, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang

Then we design a novel omnidirectional attention mechanism in OCPM to capture the context information in the permutation.


A Deep Behavior Path Matching Network for Click-Through Rate Prediction

no code implementations1 Feb 2023 Jian Dong, Yisong Yu, Yapeng Zhang, Yimin Lv, Shuli Wang, Beihong Jin, Yongkang Wang, Xingxing Wang, Dong Wang

User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making.

Click-Through Rate Prediction Contrastive Learning +1

NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

no code implementations20 May 2022 Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu, Yongkang Wang, Xingxing Wang, Dong Wang

Although VCG-based multi-slot auctions (e. g., VCG, WVCG) make it theoretically possible to model global externalities (e. g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare.

Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation

no code implementations2 Apr 2022 Ze Wang, Guogang Liao, Xiaowen Shi, Xiaoxu Wu, Chuheng Zhang, Bingqi Zhu, Yongkang Wang, Xingxing Wang, Dong Wang

Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot.

reinforcement-learning Reinforcement Learning (RL)

Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks

no code implementations2 Apr 2022 Ze Wang, Guogang Liao, Xiaowen Shi, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e. g., e-commerce and news feed sites).

Contrastive Learning Reinforcement Learning (RL)

Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation

no code implementations1 Apr 2022 Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem.

Click-Through Rate Prediction reinforcement-learning +1

Cross DQN: Cross Deep Q Network for Ads Allocation in Feed

1 code implementation9 Sep 2021 Guogang Liao, Ze Wang, Xiaoxu Wu, Xiaowen Shi, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang

Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments.

Recent Advances in Convolutional Neural Networks

no code implementations22 Dec 2015 Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.

speech-recognition Speech Recognition

Learning Fine-grained Features via a CNN Tree for Large-scale Classification

no code implementations14 Nov 2015 Zhenhua Wang, Xingxing Wang, Gang Wang

The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes.

General Classification Image Classification

Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks

no code implementations13 Sep 2015 Zhen Zuo, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang

In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs).

General Classification Image Classification

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