1 code implementation • 28 Oct 2024 • Yejing Wang, Dong Xu, Xiangyu Zhao, Zhiren Mao, Peng Xiang, Ling Yan, Yao Hu, Zijian Zhang, Xuetao Wei, Qidong Liu
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings.
no code implementations • 21 Oct 2024 • Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).
no code implementations • 14 Oct 2024 • Zhiyun Song, Yinjie Zhao, Xiaomin Li, Manman Fei, Xiangyu Zhao, Mengjun Liu, Cunjian Chen, Chung-Hsing Yeh, Qian Wang, Guoyan Zheng, Songtao Ai, Lichi Zhang
High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various medical image analysis tasks.
no code implementations • 9 Oct 2024 • Jiashi Gao, Ziwei Wang, Xiangyu Zhao, Xin Yao, Xuetao Wei
However, these studies primarily focus on perturbing accuracy, leaving a critical question unexplored: Can an attacker bypass the group fairness mechanisms in FL and manipulate the global model to be biased?
1 code implementation • 30 Sep 2024 • Qidong Liu, Xian Wu, Wanyu Wang, Yejing Wang, Yuanshao Zhu, Xiangyu Zhao, Feng Tian, Yefeng Zheng
RAT refines the embeddings to be optimally suited for SRS.
no code implementations • 2 Sep 2024 • Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, Guandong Xu
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios.
1 code implementation • 21 Aug 2024 • Xiangyu Zhao, Yuehan Zhang, Wenlong Zhang, Xiao-Ming Wu
In this work, we present UniFashion, a unified framework that simultaneously tackles the challenges of multimodal generation and retrieval tasks within the fashion domain, integrating image generation with retrieval tasks and text generation tasks.
no code implementations • 21 Aug 2024 • Xiao Han, Chen Zhu, Xiangyu Zhao, HengShu Zhu
Visual geo-localization demands in-depth knowledge and advanced reasoning skills to associate images with precise real-world geographic locations.
1 code implementation • 21 Aug 2024 • Ziwei Liu, Qidong Liu, Yejing Wang, Wanyu Wang, Pengyue Jia, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences.
no code implementations • 19 Aug 2024 • Xiao Han, Zijian Zhang, Xiangyu Zhao, Guojiang Shen, Xiangjie Kong, Xuetao Wei, Liqiang Nie, Jieping Ye
As urban residents demand higher travel quality, vehicle dispatch has become a critical component of online ride-hailing services.
1 code implementation • 16 Aug 2024 • Kai Li, Jingbo Chen, Yupeng Deng, Yu Meng, Diyou Liu, Junxian Ma, Chenhao Wang, Xiangyu Zhao
Building Footprint Extraction (BFE) from off-nadir aerial images often involves roof segmentation and offset prediction to adjust roof boundaries to the building footprint.
no code implementations • 13 Aug 2024 • Yusheng Lu, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan
And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template.
1 code implementation • 24 Jul 2024 • Shuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, Jian Weng
We show that our \textsc{sparseKT} is able to help attentional KT models get rid of irrelevant student interactions and have comparable predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets.
no code implementations • 22 Jul 2024 • Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Quoc Viet Hung Nguyen, Hongzhi Yin
Although a few methods attempt to mitigate this by employing embedding size search strategies to assign different embedding dimensions in streaming recommendations, they assume that the embedding size grows with the frequency of users/items, which eventually still exceeds the predefined memory budget over time.
no code implementations • 22 Jul 2024 • Xihong Yang, Yiqi Wang, Jin Chen, Wenqi Fan, Xiangyu Zhao, En Zhu, Xinwang Liu, Defu Lian
To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR.
1 code implementation • 16 Jul 2024 • Haodong Duan, Junming Yang, Yuxuan Qiao, Xinyu Fang, Lin Chen, YuAn Liu, Amit Agarwal, Zhe Chen, Mo Li, Yubo Ma, Hailong Sun, Xiangyu Zhao, Junbo Cui, Xiaoyi Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, Dahua Lin, Kai Chen
Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research.
no code implementations • 28 Jun 2024 • Yicheng Chen, Xiangtai Li, Yining Li, Yanhong Zeng, Jianzong Wu, Xiangyu Zhao, Kai Chen
In particular, we present a new metric, Composite Layout and Image Score (CLIS), to evaluate the generated images fairly.
1 code implementation • 25 Jun 2024 • Xiangyu Zhao, Xiangtai Li, Haodong Duan, Haian Huang, Yining Li, Kai Chen, Hua Yang
We propose the integration of an additional high-resolution visual encoder to capture fine-grained details, which are then fused with base visual features through a Conv-Gate fusion network.
Ranked #54 on Visual Question Answering on MM-Vet
1 code implementation • 24 Jun 2024 • Xiao Han, Chen Zhu, Xiao Hu, Chuan Qin, Xiangyu Zhao, HengShu Zhu
To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information.
1 code implementation • 20 Jun 2024 • Xinyu Fang, Kangrui Mao, Haodong Duan, Xiangyu Zhao, Yining Li, Dahua Lin, Kai Chen
The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.
no code implementations • 18 Jun 2024 • Yuhao Wang, Yichao Wang, Zichuan Fu, Xiangyang Li, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve their recommendation performance.
no code implementations • 18 Jun 2024 • Jingtong Gao, Bo Chen, Xiangyu Zhao, Weiwen Liu, Xiangyang Li, Yichao Wang, Zijian Zhang, Wanyu Wang, Yuyang Ye, Shanru Lin, Huifeng Guo, Ruiming Tang
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.
1 code implementation • 10 Jun 2024 • Ziru Liu, Shuchang Liu, Bin Yang, Zhenghai Xue, Qingpeng Cai, Xiangyu Zhao, Zijian Zhang, Lantao Hu, Han Li, Peng Jiang
Recommender systems aim to fulfill the user's daily demands.
1 code implementation • 6 Jun 2024 • Sheng Zhang, Maolin Wang, Xiangyu Zhao
In the rapidly evolving field of artificial intelligence, transformer-based models have gained significant attention in the context of Sequential Recommender Systems (SRSs), demonstrating remarkable proficiency in capturing user-item interactions.
1 code implementation • 31 May 2024 • Qidong Liu, Xian Wu, Xiangyu Zhao, Yejing Wang, Zijian Zhang, Feng Tian, Yefeng Zheng
These challenges, termed as the long-tail user and long-tail item dilemmas, often create obstacles for traditional SRS methods.
1 code implementation • 23 May 2024 • Pengyue Jia, Yiding Liu, Xiaopeng Li, Yuhao Wang, Yantong Du, Xiao Han, Xuetao Wei, Shuaiqiang Wang, Dawei Yin, Xiangyu Zhao
Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth.
1 code implementation • 29 Apr 2024 • Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai
M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.
no code implementations • 23 Apr 2024 • Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, Yuxuan Liang
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses.
no code implementations • 16 Apr 2024 • Chenggian Ma, Xiangyu Zhao, Chunhui Zhang, Yanzhao Qin, Wentao Zhang
With the development of Large Language Models (LLM), numerous prompts have been proposed, each with a rich set of features and their own merits.
1 code implementation • 4 Apr 2024 • Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
no code implementations • 31 Mar 2024 • Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
Recommender systems aim to predict user interest based on historical behavioral data.
2 code implementations • 19 Mar 2024 • Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Yichao Wang, Bo Chen, Wanyu Wang, Huifeng Guo, Ruiming Tang
Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.
no code implementations • 7 Mar 2024 • Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong liu, Xiangyu Zhao, Wayne Xin Zhao, Yang song, Xiangnan He
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.
1 code implementation • 7 Mar 2024 • Chi Zhang, Qilong Han, Rui Chen, Xiangyu Zhao, Peng Tang, Hongtao Song
In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs.
1 code implementation • 4 Mar 2024 • Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
1 code implementation • 28 Feb 2024 • Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Wanyu Wang, Yuyang Ye, Xiangyu Zhao, Enhong Chen, Yefeng Zheng
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
no code implementations • 27 Feb 2024 • Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi Wang, Ming He, Zitao Liu, Hongzhi Yin
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
1 code implementation • 20 Feb 2024 • Xiangyu Zhao, Zehui Li, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
In this paper, we present Topological Augmentation (TopoAug), a novel graph augmentation method that builds a combinatorial complex from the original graph by constructing virtual hyperedges directly from the raw data.
no code implementations • 16 Feb 2024 • Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo, Xiangyu Zhao
To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein.
no code implementations • 14 Feb 2024 • Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, Devendra Yadav, Fei Wang, Zhen Wen, Jiliang Tang, Hui Liu
This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations.
1 code implementation • 5 Feb 2024 • Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, Yefeng Zheng
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
no code implementations • 1 Feb 2024 • Sheng Zhang, Maolin Wang, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao, Zijian Zhang, Hongzhi Yin
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
no code implementations • 1 Feb 2024 • Maolin Wang, Yu Pan, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu, Langming Liu
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
2 code implementations • 4 Jan 2024 • Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC).
1 code implementation • 29 Dec 2023 • Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, Enhong Chen
Information extraction (IE) aims to extract structural knowledge from plain natural language texts.
no code implementations • 24 Dec 2023 • Xiaopeng Li, Lixin Su, Pengyue Jia, Xiangyu Zhao, Suqi Cheng, Junfeng Wang, Dawei Yin
To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.
1 code implementation • 17 Dec 2023 • Zichuan Fu, Xiangyang Li, Chuhan Wu, Yichao Wang, Kuicai Dong, Xiangyu Zhao, Mengchen Zhao, Huifeng Guo, Ruiming Tang
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
no code implementations • 10 Dec 2023 • Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao
In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.
2 code implementations • 5 Dec 2023 • Xinhang Li, Chong Chen, Xiangyu Zhao, Yong Zhang, Chunxiao Xing
Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency.
no code implementations • 17 Nov 2023 • Maolin Wang, Dun Zeng, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
1 code implementation • 14 Nov 2023 • Yitao Zhu, Zhenrong Shen, Zihao Zhao, Sheng Wang, Xin Wang, Xiangyu Zhao, Dinggang Shen, Qian Wang
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
no code implementations • 14 Nov 2023 • Zhiyun Song, Zengxin Qi, Xin Wang, Xiangyu Zhao, Zhenrong Shen, Sheng Wang, Manman Fei, Zhe Wang, Di Zang, Dongdong Chen, Linlin Yao, Qian Wang, Xuehai Wu, Lichi Zhang
Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI).
no code implementations • 1 Nov 2023 • You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang, Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo, Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin, Chan, Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia Jin, Guannan Zhang, Xiaodong Zeng
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
no code implementations • 30 Oct 2023 • Jialin Liu, Xinyan Su, Peng Zhou, Xiangyu Zhao, Jun Li
Mitigation of the survivor bias is achieved though counterfactual consistency.
no code implementations • 30 Oct 2023 • Jialin Liu, Xinyan Su, Zeyu He, Xiangyu Zhao, Jun Li
In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning.
1 code implementation • 29 Oct 2023 • Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li, Changying Hao, Shuaiqiang Wang, Dawei Yin
While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.
1 code implementation • 28 Oct 2023 • Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, Ruocheng Guo
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
no code implementations • 24 Oct 2023 • Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, James J. Q. Yu
Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.
3 code implementations • 21 Oct 2023 • Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng
To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.
1 code implementation • 13 Oct 2023 • Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi, Xiao-Ming Wu
We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs), Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities, EasyGen leverages BiDiffuser, a bidirectional conditional diffusion model, to foster more efficient modality interactions.
no code implementations • 23 Sep 2023 • Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao
To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.
1 code implementation • NeurIPS 2023 • Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
2 code implementations • 22 Sep 2023 • Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian
However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems.
1 code implementation • 20 Sep 2023 • Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
1 code implementation • 18 Sep 2023 • Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
2 code implementations • 12 Sep 2023 • Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang
Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains.
no code implementations • 5 Sep 2023 • Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang
To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.
1 code implementation • 2 Sep 2023 • Xiangyu Zhao, Sheng Wang, Zhiyun Song, Zhenrong Shen, Linlin Yao, Haolei Yuan, Qian Wang, Lichi Zhang
To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance.
no code implementations • 23 Aug 2023 • Xiandong Zou, Xiangyu Zhao, Pietro Liò, Yiren Zhao
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
no code implementations • 14 Aug 2023 • Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.
1 code implementation • 10 Jul 2023 • Yuqi Hu, Xiangyu Zhao, Gaowei Qing, Kai Xie, Chenglei Liu, Lichi Zhang
The regressed trabecular parameters were further used for knee osteoarthritis classification.
no code implementations • 5 Jul 2023 • Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
no code implementations • 3 Jul 2023 • Xinhang Li, Xiangyu Zhao, Yejing Wang, Yu Liu, Yong Li, Cheng Long, Yong Zhang, Chunxiao Xing
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.
no code implementations • 8 Jun 2023 • Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
no code implementations • 5 Jun 2023 • Maolin Wang, Yaoming Zhen, Yu Pan, Yao Zhao, Chenyi Zhuang, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao
THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.
1 code implementation • NeurIPS 2023 • Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James J. Q. Yu
In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj).
no code implementations • 20 Apr 2023 • Zheren Li, Zhiming Cui, Lichi Zhang, Sheng Wang, Chenjin Lei, Xi Ouyang, Dongdong Chen, Xiangyu Zhao, Yajia Gu, Zaiyi Liu, Chunling Liu, Dinggang Shen, Jie-Zhi Cheng
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
no code implementations • 16 Apr 2023 • Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
no code implementations • 7 Apr 2023 • Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.
1 code implementation • 20 Mar 2023 • Xinhang Li, Xiangyu Zhao, Jiaxing Xu, Yong Zhang, Chunxiao Xing
To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities.
no code implementations • 12 Mar 2023 • Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang
In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.
1 code implementation • 11 Mar 2023 • Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, Dawei Yin
However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.
no code implementations • 11 Mar 2023 • Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo
Sequential recommender systems aim to predict users' next interested item given their historical interactions.
1 code implementation • 12 Feb 2023 • Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, Li Li
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).
2 code implementations • 8 Feb 2023 • Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
no code implementations • 7 Feb 2023 • Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge.
1 code implementation • 7 Feb 2023 • Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
1 code implementation • 7 Feb 2023 • Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
1 code implementation • 27 Jan 2023 • Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Liò
Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.
1 code implementation • 13 Jan 2023 • Xiangyu Zhao, Zengxin Qi, Sheng Wang, Qian Wang, Xuehai Wu, Ying Mao, Lichi Zhang
However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches.
1 code implementation • 20 Dec 2022 • Jiajun Zhou, Chenxuan Xie, Shengbo Gong, Zhenyu Wen, Xiangyu Zhao, Qi Xuan, Xiaoniu Yang
To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques.
no code implementations • 21 Sep 2022 • Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
no code implementations • 16 Aug 2022 • George Boateng, Xiangyu Zhao, Malgorzata Speichert, Elgar Fleisch, Janina Lüscher, Theresa Pauly, Urte Scholz, Guy Bodenmann, Tobias Kowatsch
Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners.
no code implementations • 12 Aug 2022 • Xiangyu Zhao, Di Zang, Sheng Wang, Zhenrong Shen, Kai Xuan, Zeyu Wei, Zhe Wang, Ruizhe Zheng, Xuehai Wu, Zheren Li, Qian Wang, Zengxin Qi, Lichi Zhang
To address these issues, we propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
no code implementations • 24 Jul 2022 • Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, Guandong Xu
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data.
no code implementations • 21 Jul 2022 • Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
no code implementations • 1 Jul 2022 • Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu
In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.
1 code implementation • 24 Jun 2022 • Qiongqiong Liu, Yaying Huang, Zitao Liu, Shuyan Huang, Jiahao Chen, Xiangyu Zhao, Guimin Lin, Yuyu Zhou, Weiqi Luo
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
no code implementations • 23 May 2022 • Xin Wang, Sheng Wang, Honglin Xiong, Kai Xuan, Zixu Zhuang, Mengjun Liu, Zhenrong Shen, Xiangyu Zhao, Lichi Zhang, Qian Wang
Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution.
no code implementations • 25 Apr 2022 • Muyang Li, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, Ruocheng Guo
In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features.
1 code implementation • 19 Apr 2022 • Yejing Wang, Xiangyu Zhao, Tong Xu, Xian Wu
Thereby, feature selection is a critical process in developing deep learning-based recommender systems.
no code implementations • 4 Apr 2022 • Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang
Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.
1 code implementation • 25 Feb 2022 • Xiangyu Zhao, Sean B. Holden
In this paper, we present Meowjong, an AI for Sanma using deep reinforcement learning.
1 code implementation • 10 Oct 2021 • Xiangyu Zhao, Peng Zhang, Fan Song, Chenbin Ma, Guangda Fan, Yangyang Sun, Youdan Feng, Guanglei Zhang
The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks.
1 code implementation • 12 Aug 2021 • Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
no code implementations • 12 Jun 2021 • Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang
Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
1 code implementation • ICLR 2022 • Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
no code implementations • 4 May 2021 • Lixin Zou, Long Xia, Linfang Hou, Xiangyu Zhao, Dawei Yin
This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.
1 code implementation • 10 Feb 2021 • Xiangyu Zhao, Peng Zhang, Fan Song, Guangda Fan, Yangyang Sun, Yujia Wang, Zheyuan Tian, Luqi Zhang, Guanglei Zhang
In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.
1 code implementation • 10 Jan 2021 • Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
no code implementations • 1 Nov 2020 • Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang
Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deployed in industrial products for improving the quality of service.
1 code implementation • 4 Jul 2020 • Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, Dawei Yin
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
no code implementations • 26 Jun 2020 • Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
1 code implementation • 17 May 2020 • Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
no code implementations • 28 Feb 2020 • Xiangyu Zhao, Xudong Zheng, Xiwang Yang, Xiaobing Liu, Jiliang Tang
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
no code implementations • 26 Feb 2020 • Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, Jiliang Tang
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
no code implementations • IJCNLP 2019 • Jinxin Chang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Ting Yang, Ruifang Wang
However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.
no code implementations • 9 Sep 2019 • Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, Hui Liu
However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).
no code implementations • 27 Jun 2019 • Xiangyu Zhao, Long Xia, Lixin Zou, Dawei Yin, Jiliang Tang
Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
no code implementations • 11 Feb 2019 • Xiangyu Zhao, Long Xia, Linxin Zou, Hui Liu, Dawei Yin, Jiliang Tang
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.
Multi-agent Reinforcement Learning Recommendation Systems +2
no code implementations • 18 Dec 2018 • Xiangyu Zhao, Long Xia, Jiliang Tang, Dawei Yin
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.
no code implementations • 7 May 2018 • Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang
In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.
no code implementations • 19 Feb 2018 • Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, Dawei Yin
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
7 code implementations • 30 Dec 2017 • Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, Jiliang Tang
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.