1 code implementation • 22 Aug 2024 • Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du
Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases.
1 code implementation • 17 Jun 2024 • Haoyi Zhang, Guohao Sun, Jinhu Lu, Guanfeng Liu, Xiu Susie Fang
Researchers have attempted to enhance LLMs-based recommendation performance by incorporating information from conventional SR models.
no code implementations • 10 Mar 2024 • Pengfei Ding, Yan Wang, Guanfeng Liu
In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects.
1 code implementation • 7 Feb 2024 • Rongwei Xu, Guanfeng Liu, Yan Wang, Xuyun Zhang, Kai Zheng, Xiaofang Zhou
In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations.
no code implementations • 7 Jan 2024 • Pengfei Ding, Yan Wang, Guanfeng Liu, Nan Wang, Xiaofang Zhou
To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF.
1 code implementation • 22 Oct 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng
In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions.
no code implementations • 21 Oct 2023 • Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou
In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views.
no code implementations • 10 Aug 2023 • Pengfei Ding, Yan Wang, Guanfeng Liu
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges.
no code implementations • 17 Jun 2023 • Jiaan Wang, Jianfeng Qu, Yunlong Liang, Zhixu Li, An Liu, Guanfeng Liu, Xin Zheng
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.
1 code implementation • 7 May 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Xiaofang Zhou
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
1 code implementation • 28 Apr 2023 • Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Victor S. Sheng
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
1 code implementation • 22 Apr 2023 • Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Victor S. Sheng, Lei Zhao
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
1 code implementation • 18 Apr 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Victor S. Sheng
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
1 code implementation • 16 Apr 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
no code implementations • 13 Feb 2023 • Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
1 code implementation • 8 Aug 2022 • Hanwen Du, Hui Shi, Pengpeng Zhao, Deqing Wang, Victor S. Sheng, Yanchi Liu, Guanfeng Liu, Lei Zhao
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.
no code implementations • 11 Jul 2022 • Pengfei Ding, Yan Wang, Guanfeng Liu, Xiaofang Zhou
In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data.
no code implementations • 21 Apr 2022 • Yongjing Hao, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Deqing Wang, Lei Zhao, Yanchi Liu, Victor S. Sheng
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
no code implementations • 20 Nov 2021 • Yaxing Fang, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Lei Zhao, Xiaofang Zhou
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings.
no code implementations • 20 Nov 2021 • Yunyi Li, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Xiaofang Zhou
In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.
no code implementations • 18 Aug 2021 • Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu
Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.
no code implementations • 2 Mar 2021 • Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.
no code implementations • 20 Nov 2020 • Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
1 code implementation • 14 Sep 2020 • Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
no code implementations • ACL 2019 • He Zhao, Lan Du, Guanfeng Liu, Wray Buntine
Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models.