1 code implementation • 25 Mar 2024 • Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
1 code implementation • 14 Mar 2024 • Yinan Deng, Jiahui Wang, Jingyu Zhao, Xinyu Tian, Guangyan Chen, Yi Yang, Yufeng Yue
In this work, we propose OpenGraph, the first open-vocabulary hierarchical graph representation designed for large-scale outdoor environments.
1 code implementation • 8 Feb 2023 • Yanwen Fang, Yuxi Cai, Jintai Chen, Jingyu Zhao, Guangjian Tian, Guodong Li
Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields.
1 code implementation • NeurIPS 2021 • Jingyu Zhao, Yanwen Fang, Guodong Li
This paper introduces a concept of layer aggregation to describe how information from previous layers can be reused to better extract features at the current layer.
1 code implementation • 7 Jun 2021 • Fengtong Xiao, Lin Li, Weinan Xu, Jingyu Zhao, Xiaofeng Yang, Jun Lang, Hao Wang
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
1 code implementation • ICML 2020 • Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian
The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications.
no code implementations • 6 Sep 2019 • Di Wang, Feiqing Huang, Jingyu Zhao, Guodong Li, Guangjian Tian
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence.