1 code implementation • 10 Sep 2024 • Qitao Qin, Yucong Luo, Mingyue Cheng, Qingyang Mao, Chenyi Lei
Driven by these observations, we focus on investigating targeted attacks in FSR and propose a novel dualview attack framework, named DV-FSR.
2 code implementations • 3 Sep 2024 • Shuo Yu, Mingyue Cheng, Jiqian Yang, Jie Ouyang, Yucong Luo, Chenyi Lei, Qi Liu, Enhong Chen
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach for mitigating the hallucination of large language models (LLMs) through the integration of external knowledge.
1 code implementation • 29 Jul 2024 • Yimeng Bai, Yang Zhang, Fuli Feng, Jing Lu, Xiaoxue Zang, Chenyi Lei, Yang song
GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios.
1 code implementation • 9 Sep 2023 • Yang Jin, Kun Xu, Liwei Chen, Chao Liao, Jianchao Tan, Quzhe Huang, Bin Chen, Chenyi Lei, An Liu, Chengru Song, Xiaoqiang Lei, Di Zhang, Wenwu Ou, Kun Gai, Yadong Mu
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
no code implementations • 28 Feb 2023 • Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei, Fei Fang
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
no code implementations • 24 Aug 2022 • Yuanliang Zhang, XiaoFeng Wang, Jinxin Hu, Ke Gao, Chenyi Lei, Fei Fang
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
no code implementations • 30 May 2022 • Yixin Zhang, Yong liu, Yonghui Xu, Hao Xiong, Chenyi Lei, wei he, Lizhen Cui, Chunyan Miao
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
no code implementations • 19 Apr 2021 • Chenyi Lei, Shixian Luo, Yong liu, Wanggui He, Jiamang Wang, Guoxin Wang, Haihong Tang, Chunyan Miao, Houqiang Li
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
no code implementations • 23 Oct 2020 • Yong liu, Susen Yang, Chenyi Lei, Guoxin Wang, Haihong Tang, Juyong Zhang, Aixin Sun, Chunyan Miao
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
no code implementations • CVPR 2016 • Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, Houqiang Li
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.