no code implementations • 2 Dec 2024 • Mengtong Li, Tao Zhuang, Kai Chen, Jia-Xin Zhong, Jing Lu
Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process.
no code implementations • 14 Jul 2024 • Tao Zhuang, Jia-Xin Zhong, Jing Lu
The performance and robustness of the proposed ACC-based SZC using PAL arrays are investigated through simulations, and the results are compared with those obtained using EDL arrays.
no code implementations • 21 May 2023 • Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
no code implementations • 21 May 2023 • Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng, Dimin Wang, Hao Chen, Lixiang Lai, Tao Zhuang, Junfeng Ge
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
no code implementations • 1 Mar 2023 • Shanshan Lyu, Qiwei Chen, Tao Zhuang, Junfeng Ge
Although existing methods ESMM and ESM2 train with all impression samples over the entire space by modeling user behavior paths, SSB and DS problems still exist.
no code implementations • 30 Jan 2023 • Xiaoyang Zheng, Zilong Wang, Ke Xu, Sen Li, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng
Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase.
no code implementations • 19 Oct 2022 • Xu Yuan, Chen Xu, Qiwei Chen, Tao Zhuang, Hongjie Chen, Chao Li, Junfeng Ge
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
no code implementations • 9 Oct 2022 • Yukun Zheng, Jiang Bian, Guanghao Meng, Chao Zhang, Honggang Wang, Zhixuan Zhang, Sen Li, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
2 code implementations • 25 Sep 2022 • Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, Junfeng Ge
The results verify that the proposed model outperforms existing CTR models considerably, in terms of both CTR prediction performance and online cost-efficiency.
1 code implementation • 29 Mar 2022 • Zhifang Fan, Dan Ou, Yulong Gu, Bairan Fu, Xiang Li, Wentian Bao, Xin-yu Dai, Xiaoyi Zeng, Tao Zhuang, Qingwen Liu
In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence.
1 code implementation • 10 Feb 2022 • Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He
On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.
1 code implementation • 6 Dec 2020 • Jia-Qi Yang, Xiang Li, Shuguang Han, Tao Zhuang, De-Chuan Zhan, Xiaoyi Zeng, Bin Tong
To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
1 code implementation • NeurIPS 2020 • Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, Xiang Li
Experimentally, we show that structured pruning using polarization regularizer achieves much better results than using L1 regularizer.
no code implementations • 24 Feb 2019 • Ryuichi Takanobu, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, Bo Zheng
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+4
no code implementations • TACL 2015 • Haitong Yang, Tao Zhuang, Cheng-qing Zong
Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data.