no code implementations • 21 Apr 2024 • Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
no code implementations • 6 Mar 2024 • Antonios Valkanas, Yuening Wang, Yingxue Zhang, Mark Coates
Every day the volume of training data is expanding and the number of user interactions is constantly increasing.
no code implementations • 2 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.
no code implementations • 30 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.
no code implementations • 8 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.
2 code implementations • 7 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.
no code implementations • 6 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.
no code implementations • 15 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.
no code implementations • 2 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.
1 code implementation • 21 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.
1 code implementation • 4 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''.
no code implementations • 22 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.
no code implementations • 6 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.
no code implementations • 29 Dec 2022 • Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang, Jie Chuai, Zhitang Chen, Mark Coates, Jianye Hao, Yanhui Geng
This performance relies heavily on the configuration of the network parameters.
no code implementations • 30 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).
no code implementations • 17 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.
no code implementations • 11 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.
2 code implementations • 30 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.
Ranked #1 on Dynamic Link Prediction on Social Evolution
1 code implementation • 9 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.
Ranked #2 on Click-Through Rate Prediction on KDD12
no code implementations • 4 Aug 2022 • Florence Regol, Soumyasundar Pal, Jianing Sun, Yingxue Zhang, Yanhui Geng, Mark Coates
In this work, we introduce the node copying model for constructing a distribution over graphs.
no code implementations • 3 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.
1 code implementation • 2 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.
no code implementations • 5 Jun 2022 • Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
no code implementations • 23 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.
no code implementations • 3 Dec 2021 • Yankai Chen, Yifei Zhang, Yingxue Zhang, Huifeng Guo, Jingjie Li, Ruiming Tang, Xiuqiang He, Irwin King
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
no code implementations • 10 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.
no code implementations • 25 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.
1 code implementation • 11 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.
no code implementations • 14 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.
1 code implementation • 10 Jun 2021 • Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
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.
no code implementations • 1 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.
1 code implementation • 17 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.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 1 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.
no code implementations • 10 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.
1 code implementation • 25 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.
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.
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.
no code implementations • 23 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.
no code implementations • 1 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.
1 code implementation • 26 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.
no code implementations • 21 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.
1 code implementation • 28 Nov 2018 • Yingxue Zhang, Michael Rabbat
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph.
1 code implementation • 27 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.
1 code implementation • 27 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.