Search Results for author: Yingxue Zhang

Found 45 papers, 14 papers with code

Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization

no code implementations2 Feb 2024 Mahdi Biparva, Raika Karimi, Faezeh Faez, Yingxue Zhang

Furthermore, we illustrate the underlying aspects of the proposed model in effectively capturing extensive temporal dependencies in dynamic graphs.

Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems

no code implementations30 Nov 2023 Hongjian Gu, Yaochen Hu, Yingxue Zhang

Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios.

Collaborative Filtering Recommendation Systems

TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models

no code implementations8 Nov 2023 Zhen Yang, Yingxue Zhang, Fandong Meng, Jie zhou

Specifically, for the input from any modality, TEAL first discretizes it into a token sequence with the off-the-shelf tokenizer and embeds the token sequence into a joint embedding space with a learnable embedding matrix.

Multi-resolution Time-Series Transformer for Long-term Forecasting

no code implementations7 Nov 2023 Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates

Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.

Time Series Time Series Forecasting

Towards Automated Negative Sampling in Implicit Recommendation

no code implementations6 Nov 2023 Fuyuan Lyu, Yaochen Hu, Xing Tang, Yingxue Zhang, Ruiming Tang, Xue Liu

Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.

AutoML

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

no code implementations15 Aug 2023 Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma

The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.

Recommendation Systems

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

no code implementations2 May 2023 Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates

In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.

Incremental Learning Knowledge Distillation +1

Dynamically Expandable Graph Convolution for Streaming Recommendation

1 code implementation21 Mar 2023 Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma

Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.

Graph Learning Recommendation Systems

Compressed Interaction Graph based Framework for Multi-behavior Recommendation

1 code implementation4 Mar 2023 Wei Guo, Chang Meng, Enming Yuan, ZhiCheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang

However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.

Multi-Task Learning

A Survey on User Behavior Modeling in Recommender Systems

no code implementations22 Feb 2023 ZhiCheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, Ruiming Tang

Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.

Recommendation Systems

Spectral Augmentations for Graph Contrastive Learning

no code implementations6 Feb 2023 Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates

Contrastive learning has emerged as a premier method for learning representations with or without supervision.

Contrastive Learning Graph Embedding +1

Findings of the WMT 2022 Shared Task on Translation Suggestion

no code implementations30 Nov 2022 Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie zhou

We report the result of the first edition of the WMT shared task on Translation Suggestion (TS).

Machine Translation Task 2 +1

Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation

no code implementations17 Nov 2022 Mehrtash Mehrabi, Yingxue Zhang

One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types.

Graph Learning graph partitioning +1

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

DyG2Vec: Efficient Representation Learning for Dynamic Graphs

2 code implementations30 Oct 2022 Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates

Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.

Dynamic Link Prediction Dynamic Node Classification +2

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

1 code implementation9 Aug 2022 Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu

To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.

Click-Through Rate Prediction Recommendation Systems

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

no code implementations3 Aug 2022 Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang, Haolun Wu, Chen Gao, Dong Li, Xiu Li, Ruiming Tang

More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

no code implementations23 Dec 2021 Xiangle Cheng, James He, Shihan Xiao, Yingxue Zhang, Zhitang Chen, Pascal Poupart, FengLin Li

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.

Self-Supervised Learning

Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction

no code implementations10 Nov 2021 Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark Coates

To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.

Graph Embedding

Content Filtering Enriched GNN Framework for News Recommendation

no code implementations25 Oct 2021 Yong Gao, Huifeng Guo, Dandan Lin, Yingxue Zhang, Ruiming Tang, Xiuqiang He

It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously.

Collaborative Filtering News Recommendation

WeTS: A Benchmark for Translation Suggestion

1 code implementation11 Oct 2021 Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie zhou

To break this limitation, we create a benchmark data set for TS, called \emph{WeTS}, which contains golden corpus annotated by expert translators on four translation directions.

Machine Translation Translation

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

no code implementations14 Aug 2021 Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, Irwin King

Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.

Knowledge-Aware Recommendation Knowledge Graphs

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

1 code implementation10 Jun 2021 Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.

Bayesian Inference Spatio-Temporal Forecasting +2

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition

no code implementations NAACL 2021 Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie zhou

Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.

Relation Sentence

Dual Graph enhanced Embedding Neural Network for CTR Prediction

no code implementations1 Jun 2021 Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He

To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.

Click-Through Rate Prediction Recommendation Systems

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

1 code implementation17 Apr 2021 Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung

The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.

Decision Making Information Retrieval +4

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.

Metric Learning Recommendation Systems

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates

To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.

Knowledge Graphs Recommendation Systems

On Batch-size Selection for Stochastic Training for Graph Neural Networks

no code implementations1 Jan 2021 Yaochen Hu, Amit Levi, Ishaan Kumar, Yingxue Zhang, Mark Coates

In recent years deep learning has become an important framework for supervised learning.

MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection

no code implementations10 Oct 2020 Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie zhou

As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate.

Answer Selection Reinforcement Learning (RL)

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

1 code implementation25 Aug 2020 Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates

We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.

Incremental Learning Recommendation Systems

A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks

1 code implementation Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates

Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.

Recommendation Systems

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

no code implementations ICML 2020 Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates

Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.

Active Learning Classification +3

Non-Parametric Graph Learning for Bayesian Graph Neural Networks

no code implementations23 Jun 2020 Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates

A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.

Graph Learning Link Prediction +1

Multi-Graph Convolution Collaborative Filtering

no code implementations1 Jan 2020 Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He

In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.

Collaborative Filtering

Memory Augmented Graph Neural Networks for Sequential Recommendation

1 code implementation26 Dec 2019 Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates

In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.

Sequential Recommendation

Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

no code implementations21 Oct 2019 Yingxue Zhang, Ping Jian, Fandong Meng, Ruiying Geng, Wei Cheng, Jie zhou

Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.

Classification Discourse Parsing +3

A Graph-CNN for 3D Point Cloud Classification

1 code implementation28 Nov 2018 Yingxue Zhang, Michael Rabbat

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph.

3D Object Classification Classification +2

Bayesian graph convolutional neural networks for semi-supervised classification

1 code implementation27 Nov 2018 Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay

Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.

General Classification Graph Classification +1

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

1 code implementation27 Sep 2018 Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu

To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.

Recommendation Systems

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