Search Results for author: Ziqi Liu

Found 39 papers, 13 papers with code

MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion

2 code implementations15 Apr 2024 Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Huajun Chen, Wen Zhang

To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities.

Contrastive Learning Descriptive +3

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks

no code implementations25 Mar 2024 Dayu Shi, Xun Zhang, Ziqi Liu, Xuanbang Chen, Jianghao Li, Xiaodong Liu, William Shieh

This paper introduces a novel approach to optimize energy efficiency in wavelength division multiplexing (WDM) Visible Light Communication (VLC) systems designed for indoor broadcasting networks.

Can Small Language Models be Good Reasoners for Sequential Recommendation?

no code implementations7 Mar 2024 Yuling Wang, Changxin Tian, Binbin Hu, Yanhua Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Liang Pang, Xiao Wang

We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios.

Knowledge Distillation Sequential Recommendation

Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph

no code implementations21 Feb 2024 Qian Zhao, Hao Qian, Ziqi Liu, Gong-Duo Zhang, Lihong Gu

In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.

Language Modelling Large Language Model +1

MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

no code implementations19 Jan 2024 Hao Qian, Hongting Zhou, Qian Zhao, Hao Chen, Hongxiang Yao, Jingwei Wang, Ziqi Liu, Fei Yu, Zhiqiang Zhang, Jun Zhou

The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment.

Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

2 code implementations9 Jan 2024 Junjie Wang, Dan Yang, Binbin Hu, Yue Shen, Ziqi Liu, Wen Zhang, Jinjie Gu, Zhiqiang Zhang

Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue.

Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation

no code implementations8 Dec 2023 Chunjing Gan, Dan Yang, Binbin Hu, Ziqi Liu, Yue Shen, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Guannan Zhang

In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i. e., uncontrollable relation generation of LLMs, insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs.

graph construction Language Modelling +3

PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation

no code implementations4 Dec 2023 Chunjing Gan, Bo Huang, Binbin Hu, Jian Ma, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Guannan Zhang, Wenliang Zhong

To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent.

Recommendation Systems

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

1 code implementation13 Aug 2023 Baolin Liu, Zongyuan Yang, Pengfei Wang, Junjie Zhou, Ziqi Liu, Ziyi Song, Yan Liu, Yongping Xiong

Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods.

Image Super-Resolution

DocDiff: Document Enhancement via Residual Diffusion Models

2 code implementations6 May 2023 Zongyuan Yang, Baolin Liu, Yongping Xiong, Lan Yi, Guibin Wu, Xiaojun Tang, Ziqi Liu, Junjie Zhou, Xing Zhang

Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks.

Deblurring Denoising +1

COUPA: An Industrial Recommender System for Online to Offline Service Platforms

no code implementations25 Apr 2023 Sicong Xie, Binbin Hu, Fengze Li, Ziqi Liu, Zhiqiang Zhang, Wenliang Zhong, Jun Zhou

Aiming at helping users locally discovery retail services (e. g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems.

Position Recommendation Systems

A Benchmark for Understanding and Generating Dialogue between Characters in Stories

no code implementations18 Sep 2022 Jianzhu Yao, Ziqi Liu, Jian Guan, Minlie Huang

We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation.

Dialogue Generation Speaker Recognition

Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

no code implementations21 Jan 2022 Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu, Jun Zhou, Qinghua Zheng

Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.

Adversarial Attack Graph Learning +2

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

1 code implementation28 Dec 2021 Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen

As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.

Federated Learning Stochastic Optimization

MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data

no code implementations NeurIPS 2021 Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou

Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.

Time Series Time Series Forecasting

PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method

no code implementations22 Oct 2021 Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, Qinghua Zheng

In light of these problems, we propose a Privacy-Preserving Subgraph sampling based distributed GCN training method (PPSGCN), which preserves data privacy and significantly cuts back on communication and memory overhead.

Federated Learning Graph Learning +2

LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign

no code implementations3 Feb 2021 Tianchi Cai, Daxi Cheng, Chen Liang, Ziqi Liu, Lihong Gu, Huizhi Xie, Zhiqiang Zhang, Xiaodong Zeng, Jinjie Gu

In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.

Link Prediction Marketing

Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains

1 code implementation22 Jul 2020 Ziqi Liu, Wei Cai, Zhi-Qin John Xu

In this paper, we propose multi-scale deep neural networks (MscaleDNNs) using the idea of radial scaling in frequency domain and activation functions with compact support.

Bandit Samplers for Training Graph Neural Networks

2 code implementations NeurIPS 2020 Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).

Graph Attention

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 May 2020 Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.

Classification General Classification +2

Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

no code implementations12 Mar 2020 Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li

However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.

Privacy Preserving

Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance

no code implementations29 Feb 2020 Xia Wu, Haiyuan Liu, Ziqi Liu, Mingdong Chen, Fang Wan, Chenglong Fu, Harry Asada, Zheng Wang, Chaoyang Song

Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.

Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing

no code implementations27 Feb 2020 Ziqi Liu, Dong Wang, Qianyu Yu, Zhiqiang Zhang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, Yuan Qi

In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.

Graph Representation Learning Marketing

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

How Much Can A Retailer Sell? Sales Forecasting on Tmall

no code implementations27 Feb 2020 Chaochao Chen, Ziqi Liu, Jun Zhou, Xiaolong Li, Yuan Qi, Yujing Jiao, Xingyu Zhong

By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).

regression Time Series +1

DSSLP: A Distributed Framework for Semi-supervised Link Prediction

no code implementations27 Feb 2020 Dalong Zhang, Xianzheng Song, Ziqi Liu, Zhiqiang Zhang, Xin Huang, Lin Wang, Jun Zhou

Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure.

Link Prediction

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.

A Time Attention based Fraud Transaction Detection Framework

no code implementations26 Dec 2019 Longfei Li, Ziqi Liu, Chaochao Chen, Ya-Lin Zhang, Jun Zhou, Xiaolong Li

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.

A few results on associativity of hypermultiplications in polynomial hyperstructures over hyperfields

no code implementations21 Nov 2019 Ziqi Liu

Moreover, he shows that if $1\boxplus_{\mathbb{F}}1$ is not a singleton for hyperfield $\mathbb{F}:=(\mathbb{F},\odot,\boxplus_{\mathbb{F}}, 1, 0)$, the hypermultiplication in Poly$(\mathbb{F})$ is not associative.

Rings and Algebras

Model-Protected Multi-Task Learning

1 code implementation18 Sep 2018 Jian Liang, Ziqi Liu, Jiayu Zhou, Xiaoqian Jiang, Chang-Shui Zhang, Fei Wang

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together.

Multi-Task Learning Privacy Preserving

Feature Propagation on Graph: A New Perspective to Graph Representation Learning

no code implementations17 Apr 2018 Biao Xiang, Ziqi Liu, Jun Zhou, Xiaolong Li

In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states.

Graph Embedding Graph Representation Learning

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

Attributing Hacks

1 code implementation7 Nov 2016 Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng, Jun Zhou

That is, given properties of sites and the temporal occurrence of attacks, we are able to attribute individual attacks to joint causes and vulnerabilities, as well as estimating the evolution of these vulnerabilities over time.


Fast Differentially Private Matrix Factorization

no code implementations6 May 2015 Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed.

Collaborative Filtering

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