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- research-articleApril 2024JUST ACCEPTED
A Survey on Trustworthy Recommender Systems
- Yingqiang Ge,
- Shuchang Liu,
- Zuohui Fu,
- Juntao Tan,
- Zelong Li,
- Shuyuan Xu,
- Yunqi Li,
- Yikun Xian,
- Yongfeng Zhang
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead ...
- research-articleAugust 2023
A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 1Article No.: 29, Pages 1–29https://doi.org/10.1145/3605357State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models ...
- research-articleAugust 2023
Harnessing neighborhood modeling and asymmetry preservation for digraph representation learning
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceArticle No.: 731, Pages 6519–6524https://doi.org/10.24963/ijcai.2023/731Digraph Representation Learning aims to learn representations for directed homogeneous graphs (di-graphs). Prior work is largely constrained or has poor generalizability across tasks. Most Graph Neural Networks exhibit poor performance on digraphs due to ...
- research-articleAugust 2023
Causal Collaborative Filtering
ICTIR '23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information RetrievalPages 235–245https://doi.org/10.1145/3578337.3605122Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlative preference. However, pure correlative learning may lead to Simpson's ...
- research-articleOctober 2022
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 2732–2742https://doi.org/10.1145/3511808.3557344Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed acyclic graphs), or has poor generalizability across tasks (e.g., ...
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- research-articleOctober 2022
Dynamic Causal Collaborative Filtering
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 2301–2310https://doi.org/10.1145/3511808.3557300Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating ...
- research-articleSeptember 2022
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsPages 299–315https://doi.org/10.1145/3523227.3546767For a long time, different recommendation tasks require designing task-specific architectures and training objectives. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization ...
- research-articleJuly 2022
Explainable Fairness in Recommendation
- Yingqiang Ge,
- Juntao Tan,
- Yan Zhu,
- Yinglong Xia,
- Jiebo Luo,
- Shuchang Liu,
- Zuohui Fu,
- Shijie Geng,
- Zelong Li,
- Yongfeng Zhang
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 681–691https://doi.org/10.1145/3477495.3531973Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model ...
- research-articleApril 2022
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
WWW '22: Proceedings of the ACM Web Conference 2022Pages 1018–1027https://doi.org/10.1145/3485447.3511948Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is difficult to process and make use of the rich ...
- research-articleApril 2022
Path Language Modeling over Knowledge Graphsfor Explainable Recommendation
WWW '22: Proceedings of the ACM Web Conference 2022Pages 946–955https://doi.org/10.1145/3485447.3511937To facilitate human decisions with credible suggestions, personalized recommender systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain comprehensive information about ...
- research-articleOctober 2021
Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 494–503https://doi.org/10.1145/3459637.3482461Recent conversational recommender systems (CRS) provide a promising solution to accurately capture a user's preferences by communicating with users in natural language to interactively guide them while pro-actively eliciting their current interests. ...
- research-articleOctober 2021
Dense Contrastive Visual-Linguistic Pretraining
MM '21: Proceedings of the 29th ACM International Conference on MultimediaPages 5203–5212https://doi.org/10.1145/3474085.3475637Inspired by the success of BERT, several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale ...
- research-articleAugust 2021
EXACTA: Explainable Column Annotation
- Yikun Xian,
- Handong Zhao,
- Tak Yeon Lee,
- Sungchul Kim,
- Ryan Rossi,
- Zuohui Fu,
- Gerard de Melo,
- S. Muthukrishnan
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 3775–3785https://doi.org/10.1145/3447548.3467211Column annotation, the process of annotating tabular columns with labels, plays a fundamental role in digital marketing data governance. It has a direct impact on how customers manage their data and facilitates compliance with regulations, restrictions, ...
- short-paperJuly 2021
HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 2415–2421https://doi.org/10.1145/3404835.3463247There is increasing recognition of the need for human-centered AI that learns from human feedback. However, most current AI systems focus more on the model design, but less on human participation as part of the pipeline. In this work, we propose a Human-...
- research-articleJuly 2021
FedCT: Federated Collaborative Transfer for Recommendation
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 716–725https://doi.org/10.1145/3404835.3462825When a user starts exploring items from a new area of an e-commerce system, cross-domain recommendation techniques come into help by transferring the abundant knowledge from the user's familiar domains to this new domain. However, this solution usually ...
- research-articleJune 2021
User-oriented Fairness in Recommendation
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and ...
- research-articleJune 2021
Efficient Non-Sampling Knowledge Graph Embedding
WWW '21: Proceedings of the Web Conference 2021Pages 1727–1736https://doi.org/10.1145/3442381.3449859Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negative sampling, i.e., the model aims to maximize some similarity of the ...
- tutorialApril 2021
IUI 2021 Tutorial on Conversational Recommendation Systems
IUI '21 Companion: Companion Proceedings of the 26th International Conference on Intelligent User InterfacesPages 1–2https://doi.org/10.1145/3397482.3450621Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer ...
- tutorialMarch 2021
WSDM 2021 Tutorial on Conversational Recommendation Systems
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data MiningPages 1134–1136https://doi.org/10.1145/3437963.3441661Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer ...
- research-articleJanuary 2021
HID: hierarchical multiscale representation learning for information diffusion
IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial IntelligenceArticle No.: 468, Pages 3385–3391Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social ...