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<!DOCTYPE html>
<html lang="en-US">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width,initial-scale=1" />
<title>Guangping Zhang (张光平)</title>
<meta name="generator" content="VuePress 1.9.7" />
<link rel="icon" href="/icons/logo.svg" />
<meta name="description" content="The homepage of Guangping Zhang." />
<link rel="preload" href="/css/0.styles.06666685.css" as="style" />
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d="M436 124H12c-6.627 0-12-5.373-12-12V80c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12zm0 160H12c-6.627 0-12-5.373-12-12v-32c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12zm0 160H12c-6.627 0-12-5.373-12-12v-32c0-6.627 5.373-12 12-12h424c6.627 0 12 5.373 12 12v32c0 6.627-5.373 12-12 12z">
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<a href="/" aria-current="page" class="home-link router-link-exact-active router-link-active"><!---->
<span class="site-name">Guangping Zhang (张光平)</span></a>
</header>
<main class="page">
<div class="theme-default-content content__default">
<div class="profile">
<div class="image"><img src="/profile.jpg" alt /></div>
<div class="info">
<div class="name">Guangping Zhang (张光平)</div>
<div class="bio">
<p>Ph.D. Candidate at Fudan University</p>
</div>
<div class="socials">
<div>
<a href="https://github.com/aSeriousCoder" target="_blank"><img src="/icons/github.svg" alt="GitHub"
title="GitHub" /></a>
</div>
<div>
<a href="https://scholar.google.com/citations?user=gbasKBwAAAAJ" target="_blank"><img
src="/icons/scholar.svg" alt="Google Scholar" title="Google Scholar" /></a>
</div>
<!-- <div>
<a href="/gpzhang_cv_en.pdf" target="_blank"
><img src="/icons/cv.svg" alt="cv" title="cv"
/></a>
</div> -->
</div>
<div class="contact">
<div title="Contact me" class="email">
</div>
</div>
<!---->
</div>
</div>
<h2 id="about-me">About Me</h2>
<p>
I am a Ph.D. candidate in the
<a href="https://cscw.fudan.edu.cn" target="_blank" rel="noopener noreferrer">
Collaborative Information and Systems Laboratory (CISL)
<span>
<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
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d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
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</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
, School of Computer Science at Fudan University, advised by
<a href="https://cscw.fudan.edu.cn/ninggu/list.htm" target="_blank" rel="noopener noreferrer">Prof. Ning
Gu<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
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</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>.
</p>
<p>
My research interests include Human-centered News Recommendation,
Computer-supported Collaborative Works (CSCW) & Social Computing,
and Human-computer Interaction (HCI). I graduated from Shanghai
University of Finance and Economics (SUFE) in 2020 with a B.Eng. in
Computer Science.
</p>
<h2 id="news">News</h2>
<ul>
<li>
📌 [04/2024] We've released the
<a href="https://github.com/aSeriousCoder/SimuLine" target="_blank" rel="noopener noreferrer"> Code Repo
<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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</svg>
<span class="sr-only">(opens new window)</span></span></a>
of our TCSS paper
<a href="https://ieeexplore.ieee.org/document/10504866" target="_blank"
rel="noopener noreferrer">"Simulating News Recommendation Ecosystems for Insights and
Implications"<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false"
x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</li>
<li>
📌 [04/2024] We've released the
<a href="https://github.com/aSeriousCoder/DivHGNN" target="_blank" rel="noopener noreferrer"> Code Repo
<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
of our TWEB paper
<a href="https://dl.acm.org/doi/abs/10.1145/3649886" target="_blank"
rel="noopener noreferrer">"Heterogeneous Graph Neural Network with Personalized and Adaptive
Diversity for News Recommendation"<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true"
focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"
class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</li>
</ul>
<h2 id="education">Education</h2>
<ul>
<li>
<p>
<strong>Ph.D. Candidate,</strong> School of Computer Science,
Fudan University.
<span style="float: right">2020.9 - Present</span>
</p>
<p>
Advisor:
<a href="https://cscw.fudan.edu.cn/ninggu/list.htm" target="_blank" rel="noopener noreferrer">Prof. Ning
Gu<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</p>
</li>
<li>
<p>
<strong>Teaching Assistant - Social Computing (Undergraduate Course)</strong><span
style="float: right">2024.2 - 2024.7</span>
</p>
<p>
Advisor:
<a href="https://cscw.fudan.edu.cn/tunlu/list.htm" target="_blank" rel="noopener noreferrer">Prof. Tun
Lu<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</p>
</li>
<li>
<p>
<strong>Teaching Assistant - Social Computing (Graduate Course)</strong><span
style="float: right">2022.2 - 2022.7</span>
</p>
<p>
Advisor:
<a href="https://cscw.fudan.edu.cn/tunlu/list.htm" target="_blank" rel="noopener noreferrer">Prof. Tun
Lu<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</p>
</li>
<li>
<p>
<strong>Bachelor's Degree (with honor),</strong> School of
Information Management and Engineering, Shanghai University of
Finance and Economics.
<span style="float: right">2016.9 - 2020.6</span>
</p>
<p>GPA:3.8/4.0 | Ranking: 1/67</p>
</li>
</ul>
<h2 id="research-interests">Research Interests</h2>
<ul>
<li>Human-centered News Recommendation</li>
<li>Computer-supported Collaborative Works (CSCW) & Social Computing</li>
<li>Human-computer Interaction (HCI)</li>
</ul>
<h2 id="publications">Publications</h2>
<div class="md-card">
<div class="card-image">
<img src="/research/m2.jpg" alt />
</div>
<div class="card-content">
<ul>
<li>
<p>
<a href="https://ieeexplore.ieee.org/document/10504866" target="_blank"
rel="noopener noreferrer"><strong>[P2] Simulating News Recommendation Ecosystems for Insights and
Implications (IEEE TCSS'24)</strong><span><svg xmlns="http://www.w3.org/2000/svg"
aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15"
height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</strong>
</p>
<p>
<strong>Guangping Zhang</strong>, Dongsheng Li, Hansu Gu,
Tun Lu, Li Shang, Ning Gu.
</p>
</li>
</ul>
<p>
Studying the evolution of online news communities is essential for improving the effectiveness of news
recommender systems. Traditionally, this has been done through empirical research based on static data
analysis. While this approach has yielded valuable insights for optimizing recommender system designs,
it is limited by the lack of appropriate datasets and open platforms for controlled social experiments.
This gap in the existing literature hinders a comprehensive understanding of the impact of recommender
systems on the evolutionary process and its underlying mechanisms. As a result, suboptimal system
designs may be developed that could negatively affect long-term utilities. In this work, we propose
SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a
detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a
latent space well reflecting the human behaviors and then simulates the news recommendation ecosystem
via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis
framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the
characteristics of each evolutionary phase from the perspective of life-cycle theory and propose a
relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the
impacts of recommender system designing strategies, including the utilization of cold-start news,
breaking news, and promotion, on the evolutionary process, which sheds new light on the design of
recommender systems.
</p>
<p>
[<a href="/papers/SimuLine.pdf">PDF</a>]
<!-- [<a href="/research/m2.pdf">Slides</a>] -->
[<a href="https://github.com/aSeriousCoder/SimuLine" target="_blank"
rel="noopener noreferrer">Code<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true"
focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"
class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
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points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>]
</p>
</div>
</div>
<div class="md-card">
<div class="card-image">
<img src="/research/m2.jpg" alt />
</div>
<div class="card-content">
<ul>
<li>
<p>
<a href="https://dl.acm.org/doi/abs/10.1145/3649886" target="_blank"
rel="noopener noreferrer"><strong>[P1] Heterogeneous Graph Neural Network with Personalized and
Adaptive Diversity for News Recommendation (ACM TWEB'24)</strong><span><svg
xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px"
viewBox="0 0 100 100" width="15" height="15" class="icon outbound">
<path fill="currentColor"
d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z">
</path>
<polygon fill="currentColor"
points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9">
</polygon>
</svg>
<span class="sr-only">(opens new window)</span></span></a>
</strong>
</p>
<p>
<strong>Guangping Zhang</strong>, Dongsheng Li, Hansu Gu,
Tun Lu, Ning Gu.
</p>
</li>
</ul>
<p>
The emergence of online media has facilitated the dissemination of news, but has also introduced the
problem of information overload. To address this issue, providing users with accurate and diverse news
recommendations has become increasingly important. News possesses rich and heterogeneous content, and
the factors that attract users to news reading are varied. Consequently, accurate news recommendation
requires modeling of both the heterogeneous content of news and the heterogeneous user-news
relationships. Furthermore, users' news consumption is highly dynamic, which is reflected in the
differences in topic concentration among different users and in the real-time changes in user interests.
To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity
for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the
heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a
heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused
node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN
integrates the heterogeneous relationships to enhance node representation for accurate news
recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further
improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes
through variational representation learning for providing personalized diversity. Additionally, a
time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of
user real-time interests for providing adaptive diversity. Extensive experiments on real-world news
datasets demonstrate the effectiveness of the proposed method.
</p>
<p>
[<a href="/papers/DivHGNN.pdf">PDF</a>]
<!-- [<a href="/research/m2.pdf">Slides</a>] -->
[<a href="https://github.com/aSeriousCoder/DivHGNN" target="_blank"
rel="noopener noreferrer">Code<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true"
focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"
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2020
<a href="https://cs.fudan.edu.cn/17/79/c24256a268153/page.htm" target="_blank"
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<li>2020 Outstanding Graduate of Shanghai</li>
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