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<!DOCTYPE html>
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<title>Trustworthy Recommender Systems</title>
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<h1>Trustworthy Recommender Systems</h1>
<h2 style="color:#FFFFFF;"><b>Keywords: Recommender Systems, Robustness, Fairness, Explainability, Privacy, Environmental Well-being, Accountability, Auditability</b> </h2>
<h2 style="color:#FFFFFF;"><b>Event Date: 14:00 PM - 17:30 (Beijing Time), Saturday, August 19, 2023</b> </h2>
<h2 style="color:#FFFFFF;"><b>Address: Kokand 6202+6203, Sheraton Grand Macao, Macao, S.A.R</b> </h2>
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<h2 style="color:#FFFFFF;"><b>Zoom Link: <a href="https://acm-org.zoom.us/j/99041282188?pwd=aUhCOTd3djdBM3F5QURLZnRlV210UT09">https://acm-org.zoom.us/j/99041282188?pwd=aUhCOTd3djdBM3F5QURLZnRlV210UT09</a> </b> </h2>
-->
<h2 style="color:#FFFFFF;"><b>Our Survey Paper: <a href="https://arxiv.org/pdf/2209.10117.pdf"> A Comprehensive Survey on Trustworthy Recommender Systems
</a> </b> </h2>
<!--<div class="event-date">9:00 - 11:45 PM, April 16, 2021 (EST time)</div>-->
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<h1>INTRO</h1>
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<p class="large text-muted">
In the past few decades, recommender system has becomeone of the most important information services for AI applications. Nowadays, recommender systems as intelligent filtering tools have played an increasingly important role in people’s daily lives via their successful deployments in various user-oriented online services, such as online shopping, jobs matching, financial product recommendations, and medical recommendations. More recently, inspired by the great success of Deep Neural Networks (DNNs) in powerful representation learning abilities, DNN-based recommendation techniques have shown impressive performance across a wide range of tasks.
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<p class="large text-muted">
Although recommender systems can benefit people in daily life, recent studies show that they can have negative impacts on human beings. For example, recommender systems are highly vulnerable to adversarial attacks, where recommendation results can be manipulated by attackers with malicious desires. Meanwhile, the recommendation algorithms can inherit and even magnify the bias behaviors from training datasets, leading to discriminatory predictions for underrepresented groups. In addition, recommender systems are also vulnerable to privacy attacks. For instance, malicious attackers can infer users’ private information from public data. Due to the complicated working mechanisms, recommender systems
often make black-box decisions that are hard to be explained to various stakeholders, resulting in unreliable recommendation predictions. These untrustworthy aspects in recommender systems can make unreliable recommendation results and then produce significant harmful effects in various real-world applications, especially those in safety-critical areas such as finance and healthcare, resulting in severe economic, social, and security consequences.
</p>
<p class="large text-muted">
To mitigate these untrustworthy aspects, a great amount of research on trustworthy recommender systems has emerged in recent years. In this tutorial, we will timely provide a comprehensive overview of <strong>T</strong>rustworthy <strong>Rec</strong>ommender systems (<strong>TRec</strong>) with a specific focus on six of the most crucial aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we will summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.
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<!-- <div> Time: 4:45 PM - 6:15 PM (Beijing Time) or 10:45 AM - 12:15 PM (CEST Time), Tue. Apr 26, 2022</div>
<div>Zoom Link: <a href="https://acm-org.zoom.us/j/99041282188?pwd=aUhCOTd3djdBM3F5QURLZnRlV210UT09">https://acm-org.zoom.us/j/99041282188?pwd=aUhCOTd3djdBM3F5QURLZnRlV210UT09</a> </div>
-->
<div>Our Survey Paper: <a href="https://arxiv.org/pdf/2209.10117.pdf"> A Comprehensive Survey on Trustworthy Recommender Systems
</a></div>
<div>Our Slides: <a href="doc/IJCAI_slides.pdf">Trustworthy recommender systems: foundations and frontiers
</a> </div>
<div>Tutorial Website: <a href="https://advanced-recommender-systems.github.io/trustworthy-rec/">https://advanced-recommender-systems.github.io/trustworthy-rec/</a> </div>
</p>
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The topics of this tutorial include (but are not limited to) the following:
<ol>
<strong> <li style="color:black;">Introduction to Trustworthy Recommender System (TRec) </li> </strong>
<strong> <li style="color:black;">Safety & Robustness </li></strong>
<strong> <li style="color:black;">Non-discrimination & Fairness </li></strong>
<strong> <li style="color:black;">Explainability </li></strong>
<strong> <li style="color:black;">Privacy </li></strong>
<strong> <li style="color:black;">Environmental Well-being </li></strong>
<strong> <li style="color:black;">Accountability & Auditability </li></strong>
<strong> <li style="color:black;">Dimension Interactions & Future Directions </li></strong>
</ol>
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<h1>Six key dimensions of Trustworthy Recommender Systems (TRec)</h1>
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<!-- <h3>Xiangyu Zhao. -->
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<small>City University of Hong Kong</small>
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<a href="https://wenqifan03.github.io/"><strong>Dr. Wenqi Fan</strong></a > is a research assistant professor of the Department of Computing at The Hong Kong Polytechnic University (PolyU). He received his Ph.D. degree from the City University of Hong Kong (CityU) in 2020. From 2018 to 2020, he was a visiting research scholar at Michigan State University. His research interests are in the broad areas of machine learning and data mining, with a particular focus on Recommender Systems, Graph Neural Networks, and Adversarial Attacks. He has published innovative papers in top-tier journals and conferences such as IEEE TKDE, KDD, WWW, NeurIPS, ICDE, IJCAI, AAAI, RecSys, WSDM, and SDM. He serves as top-tier conference (senior) program committee members (e.g., ICML, ICLR, NeurIPS, KDD, WWW, AAAI, IJCAI, CIKM, WSDM, etc.), and journal reviewers (e.g., TKDE, TIST, TKDD, TOIS, TAI, etc.). He also serves as the lead tutor of tutorials in top-tier conferences (e.g., WWW 2021, IJCAI 2021, and ICAPS 2021). More information about him can be found at https://wenqifan03.github.io/.
</p>
<p class="large text-muted">
<a href="https://zhaoxyai.github.io/"><strong>Dr. Xiangyu Zhao</strong> </a >is an assistant professor of the school of data science at City University of Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU, MS (2017) at USTC and BEng (2014) at UESTC. His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; and (2) Deep Reinforcement Learning, AutoML, Trustworthy AI and Multimodal ML. He has published more than 50 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, IJCAI, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research has been awarded ICDM’21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Tencent Open Fund, Criteo Research Award, Bytedance Research Award, MSU Dissertation Fellowship, and nomination for Joint AAAI/ACM SIGAI Doctoral Dissertation Award. He serves as the organizers of DRL4KDD@KDD’19, DRL4IR@SIGIR’20, 2nd DRL4KD@WWW’21, 2nd DRL4IR@SIGIR’21, 3rd DRL4IR@SIGIR’22. Please find more information at https://zhaoxyai.github.io/. Dr. Zhao is a lead tutor at WWW’21/22 and IJCAI’21.
</p>
<p class="large text-muted">
<a href="."> <strong>Lin Wang</strong></a > is currently a PhD student at The Hong Kong Polytechnic University. He got his B.Eng. and MSc degrees at Northwestern Polytechnical University (NPU). His research interests include recommender systems and Graph Neural Networks.
</p>
<p class="large text-muted">
<a href="."> <strong>Xiao Chen</strong></a > is currently a PhD student at The Hong Kong Polytechnic University. He got his MSc degree in Zhejiang University (ZJU) and his B.Eng. at Northwestern Polytechnical University (NPU). His research interests include fairness recommender systems and Graph Neural Networks.
</p>
<p class="large text-muted">
<a href="."> <strong>Jingtong Gao</strong></a > is currently a PhD student at the City University of Hong Kong (CityU) under the advisory of Prof. Xiangyu Zhao. Prior to CityU, he got his MSc degree at Nanyang Technological University (NTU) and his B.Eng. in Beihang University (BUAA).
</p>
<p class="large text-muted">
<a href="."> <strong> Qidong Liu </strong> </a > is currently a joint-PhD with the City University of Hong Kong (CityU) and Xi’an Jiaotong University (XJTU). He got his B.Eng. degree at Xi’an Jiaotong University in 2019. His research interests include Recommender Systems and Causal Inference.
</p>
<p class="large text-muted">
<a href="."> <strong>Shijie Wang</strong></a > is currently an MPhil student of Hong Kong Polytechnic University (PolyU). He got his BSc degree at the University of Liverpool (Uol). His research interests include recommender systems and Graph Neural Networks.
</p>
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