1 code implementation • 26 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.
no code implementations • 3 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.
no code implementations • 29 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.
no code implementations • 27 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.
no code implementations • 28 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.
no code implementations • 9 Aug 2023 • Shuwei Chen, Xiang Li, Jian Dong, Jin Zhang, Yongkang Wang, Xingxing Wang
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations.
no code implementations • 4 Jul 2023 • Wei zhang, Ping Zhang, Jian Dong, Yongkang Wang, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang
The effectiveness of ad creatives is greatly influenced by their visual appearance.
no code implementations • 27 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.
no code implementations • 26 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.
no code implementations • 5 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.
1 code implementation • 1 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.
no code implementations • 17 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.
1 code implementation • 6 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.
no code implementations • 1 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.
no code implementations • 29 Jan 2023 • Xiang Li, Shuwei Chen, Jian Dong, Jin Zhang, Yongkang Wang, Xingxing Wang, Dong Wang
Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems.
no code implementations • 20 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.
no code implementations • 2 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.
no code implementations • 2 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).
no code implementations • 1 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.
1 code implementation • 9 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.
no code implementations • 22 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.
no code implementations • 14 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.
no code implementations • 13 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).
no code implementations • 18 May 2014 • Xiaojiang Peng, Li-Min Wang, Xingxing Wang, Yu Qiao
Many efforts have been made in each step independently in different scenarios and their effect on action recognition is still unknown.