Search Results for author: Xiaofang Zhou

Found 23 papers, 5 papers with code

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

no code implementations21 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.

Contrastive Learning Sequential Recommendation

Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks

no code implementations14 Aug 2023 Zhili Wang, Shimin Di, Lei Chen, Xiaofang Zhou

Given a pre-trained GNN, we propose to search to fine-tune pre-trained graph neural networks for graph-level tasks (S2PGNN), which adaptively design a suitable fine-tuning framework for the given labeled data on the downstream task.

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

no code implementations24 Jul 2023 Wei Yuan, Liang Qu, Lizhen Cui, Yongxin Tong, Xiaofang Zhou, Hongzhi Yin

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.

Knowledge Distillation Recommendation Systems

Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation

1 code implementation7 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.

Contrastive Learning Sequential Recommendation

Sequential Recommendation with Diffusion Models

no code implementations10 Apr 2023 Hanwen Du, Huanhuan Yuan, Zhen Huang, Pengpeng Zhao, Xiaofang Zhou

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation.

Sequential Recommendation

Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding

1 code implementation23 Aug 2022 Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Jianfeng Qu, Xiaofang Zhou

Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives.

Entity Alignment

Few-Shot Semantic Relation Prediction across Heterogeneous Graphs

no code implementations11 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.


Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

no code implementations1 Jul 2022 Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen

Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.

Dynamic graph embedding Graph Mining

A Learned Index for Exact Similarity Search in Metric Spaces

no code implementations21 Apr 2022 Yao Tian, Tingyun Yan, Xi Zhao, Kai Huang, Xiaofang Zhou

In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces.

BIG-bench Machine Learning

Ensemble Semi-supervised Entity Alignment via Cycle-teaching

1 code implementation12 Mar 2022 Kexuan Xin, Zequn Sun, Wen Hua, Bing Liu, Wei Hu, Jianfeng Qu, Xiaofang Zhou

We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher.

Entity Alignment Knowledge Graphs

Informed Multi-context Entity Alignment

1 code implementation2 Jan 2022 Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Xiaofang Zhou

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources.

Entity Alignment Entity Embeddings +1

Quaternion-Based Graph Convolution Network for Recommendation

no code implementations20 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.

Recommendation Systems Representation Learning

Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation

no code implementations20 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.

Sequential Recommendation

Overcoming Data Sparsity in Group Recommendation

no code implementations2 Oct 2020 Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Xiaofang Zhou

Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members.

Decision Making Graph Embedding +2

EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors

no code implementations6 Jun 2020 Yu Yang, Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Hongzhi Yin, Xiaofang Zhou

We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.

Management Network Embedding

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations7 Nov 2019 Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Recommendation Systems Test

SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding

no code implementations27 Oct 2019 Saeed Najafipour, Saeid Hosseini, Wen Hua, Mohammad Reza Kangavari, Xiaofang Zhou

Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors.

Community Detection named-entity-recognition +2

TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs

no code implementations6 Jul 2019 Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung, Hongzhi Yin, Mohammad Reza Kangavari, Xiaofang Zhou

We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes.

Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

no code implementations29 May 2019 Jian Liu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Fuzheng Zhuang, Jiajie Xu, Xiaofang Zhou, Hui Xiong

Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.

Transfer Learning

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

no code implementations13 Jul 2017 Lei Zhu, Zi Huang, Xiaobai Liu, Xiangnan He, Jingkuan Song, Xiaofang Zhou

Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.

Personalized Video Recommendation Using Rich Contents from Videos

1 code implementation21 Dec 2016 Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou

In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features.

Recommendation Systems

A Convex Formulation for Spectral Shrunk Clustering

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou

Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance.

Clustering Dimensionality Reduction

Compound Rank-k Projections for Bilinear Analysis

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Sen Wang, Yi Yang, Xiaofang Zhou, Chengqi Zhang

In many real-world applications, data are represented by matrices or high-order tensors.

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