Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleOctober 2024
A two-stage image enhancement and dynamic feature aggregation framework for gastroscopy image segmentation
AbstractAccurate and reliable automatic segmentation of lesion areas in gastroscopy images can assist endoscopists in making diagnoses and reduce the possibility of missed or incorrect diagnoses. This paper presents a two-stage framework for segmenting ...
- research-articleJuly 2024
Ratel: MPC-extensions for Smart Contracts
- Yunqi Li,
- Kyle Soska,
- Zhen Huang,
- Sylvain Bellemare,
- Mikerah Quintyne-Collins,
- Lun Wang,
- Xiaoyuan Liu,
- Dawn Song,
- Andrew Miller
ASIA CCS '24: Proceedings of the 19th ACM Asia Conference on Computer and Communications SecurityPages 336–352https://doi.org/10.1145/3634737.3661142Enhancing privacy on smart contract-enabled blockchains has garnered much attention in recent research. Zero-knowledge proofs (ZKPs) is one of the most popular approaches, however, they fail to provide full expressiveness and fine-grained privacy. To ...
- 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 ...
- surveyOctober 2023
Fairness in Recommendation: Foundations, Methods, and Applications
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 14, Issue 5Article No.: 95, Pages 1–48https://doi.org/10.1145/3610302As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the ...
- 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-articleFebruary 2023
Predicting flower induction of litchi (Litchi chinensis Sonn.) with machine learning techniques
Computers and Electronics in Agriculture (COEA), Volume 205, Issue Chttps://doi.org/10.1016/j.compag.2022.107572Highlights- Multiple factors contributed to duration of flower induction (DFI) were explored.
- Contribution of inherent tree and external environmental factors to DFI were classified.
- Optimal combination of timescales for air temperature and ...
The flower induction is a critical physiological change during which vegetative buds transit to floral buds. The duration of flower induction (DFI) for litchi plays determinative role for the success and the quality of flowering. It is hard to be ...
- research-articleApril 2024
Eliciting thinking hierarchy without a prior
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsArticle No.: 969, Pages 13329–13341When we use the wisdom of the crowds, we usually rank the answers according to their popularity, especially when we cannot verify the answers. However, this can be very dangerous when the majority make systematic mistakes. A fundamental question arises: ...
- research-articleOctober 2022
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 169–179https://doi.org/10.1145/3511808.3557385Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems. For example, the basic skills "dig hole,'' "put tree,'' "backfill'' and "...
- research-articleJune 2022
Causal factorization machine for robust recommendation
JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital LibrariesArticle No.: 10, Pages 1–9https://doi.org/10.1145/3529372.3530921Factorization Machines (FMs) are widely used for the collaborative recommendation because of their effectiveness and flexibility in feature interaction modeling. Previous FM-based works have claimed the importance of selecting useful features since ...
- research-articleMay 2022
Revenue and User Traffic Maximization in Mobile Short-Video Advertising
AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent SystemsPages 1092–1100A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform ...
- 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-articleFebruary 2022
Graph Collaborative Reasoning
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPages 75–84https://doi.org/10.1145/3488560.3498410Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffer from ...
- research-articleJanuary 2022
Multi-scale boundary neural network for gastric tumor segmentation
The Visual Computer: International Journal of Computer Graphics (VISC), Volume 39, Issue 3Pages 915–926https://doi.org/10.1007/s00371-021-02374-1AbstractAt present, gastric cancer patients account for a large proportion of all tumor patients. Gastric tumor image segmentation can provide a reliable additional basis for the clinical analysis and diagnosis of gastric cancer. However, the existing ...
- tutorialOctober 2021
CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 4857–4860https://doi.org/10.1145/3459637.3483280Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a ...
- research-articleOctober 2021
Counterfactual Explainable Recommendation
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 1784–1793https://doi.org/10.1145/3459637.3482420By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (...
- research-articleJuly 2021
Towards Personalized Fairness based on Causal Notion
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 1054–1063https://doi.org/10.1145/3404835.3462966Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in ...
- tutorialJuly 2021
Tutorial on Fairness of Machine Learning in Recommender Systems
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 2654–2657https://doi.org/10.1145/3404835.3462814Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a growing ...
- research-articleJune 2021
Neural Collaborative Reasoning
WWW '21: Proceedings of the Web Conference 2021Pages 1516–1527https://doi.org/10.1145/3442381.3449973Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that ...
- 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-articleMarch 2021
Towards Long-term Fairness in Recommendation
- Yingqiang Ge,
- Shuchang Liu,
- Ruoyuan Gao,
- Yikun Xian,
- Yunqi Li,
- Xiangyu Zhao,
- Changhua Pei,
- Fei Sun,
- Junfeng Ge,
- Wenwu Ou,
- Yongfeng Zhang
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data MiningPages 445–453https://doi.org/10.1145/3437963.3441824As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or ...