no code implementations • 19 May 2024 • Pengxiang Lan, Enneng Yang, YuTing Liu, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang
Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, whereby the number of parameters is greatly reduced as well as the training time.
no code implementations • 13 Mar 2024 • YuTing Liu, Yizhou Dang, Yuliang Liang, Qiang Liu, Guibing Guo, Jianzhe Zhao, Xingwei Wang
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i. e., links in a graph) with items.
no code implementations • 11 Mar 2024 • Yizhou Dang, YuTing Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao
Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training.
no code implementations • 18 Feb 2024 • Jinghao Zhang, YuTing Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
1 code implementation • 5 Feb 2024 • Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, DaCheng Tao
That is, there is a significant discrepancy in the representation distribution between the merged and individual models, resulting in poor performance of merged MTL.
no code implementations • 10 Nov 2023 • YuTing Liu, Enneng Yang, Yizhou Dang, Guibing Guo, Qiang Liu, Yuliang Liang, Linying Jiang, Xingwei Wang
In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of \emph{content} and \emph{structure}.
1 code implementation • 4 Oct 2023 • Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, DaCheng Tao
This approach aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data.
no code implementations • 31 Aug 2023 • Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, DaCheng Tao
Next, we train the CL model by minimizing the gap between the responses of the CL model and the black-box API on synthetic data, to transfer the API's knowledge to the CL model.
no code implementations • 26 Aug 2023 • Zichen Yuan, Qi Shen, Bingyi Zheng, YuTing Liu, Linying Jiang, Guibing Guo
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia.
no code implementations • ICCV 2023 • Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, Xingwei Wang
In this paper, we first revisit the gradient projection method from the perspective of flatness of loss surface, and find that unflatness of the loss surface leads to catastrophic forgetting of the old tasks when the projection constraint is reduced to improve the performance of new tasks.
1 code implementation • 16 Dec 2022 • Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, Hong Liu
However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}.
no code implementations • 28 Nov 2022 • Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
no code implementations • 6 Jun 2021 • Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
no code implementations • SEMEVAL 2021 • Zhixiang Chen, Yikun Lei, Pai Liu, Guibing Guo
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension.
no code implementations • 16 Nov 2020 • Ziyang Wang, Wei Wei, Xian-Ling Mao, Guibing Guo, Pan Zhou, Shanshan Feng
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.
1 code implementation • 28 Apr 2020 • Shilin Qu, Fajie Yuan, Guibing Guo, Liguang Zhang, Wei Wei
Specifically, our framework divides proximal information units into chunks, and performs memory access at certain time steps, whereby the number of memory operations can be greatly reduced.
no code implementations • 16 Feb 2020 • Enneng Yang, Xin Xin, Li Shen, Guibing Guo
In this work, we propose an alternative approach to model high-order interaction signals in the embedding level, namely Generalized Embedding Machine (GEM).
no code implementations • 19 Sep 2019 • Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
no code implementations • 11 Jun 2019 • Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong
To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.
1 code implementation • 30 Apr 2019 • Hui Fang, Danning Zhang, Yiheng Shu, Guibing Guo
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones.
no code implementations • 5 Jan 2018 • Guibing Guo, Songlin Zhai, Fajie Yuan, Yu-An Liu, Xingwei Wang
Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i. e., the gap between images' visual features (low-level) and labels' semantic features (high-level).