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- Video4.1 MBPublished By ACM
A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation ...
- Video30.4 MBPublished By ACM
Fairness through Aleatoric Uncertainty
We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group or ...
- Video37 MBPublished By ACM
STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction
The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on ...
- Video4.2 MBPublished By ACM
Virtual Node Tuning for Few-shot Node Classification
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base ...
- Video38.2 MBPublished By ACM
Learning Strong Graph Neural Networks with Weak Information
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suffer from weak information, i.e., incomplete structure, incomplete ...
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- Video4.8 MBPublished By ACM
Contrastive Meta-Learning for Few-shot Node Classification
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. To tackle such a label shortage issue, existing works generally ...
- Video232.5 MBPublished By ACM
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an ...
- Video21.2 MBPublished By ACM
Bias Mitigation for Toxicity Detection via Sequential Decisions
Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with ...
- Video102.9 MBPublished By ACM
Causal Mediation Analysis with Hidden Confounders
An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway. For instance, in causal fairness, the total effect of being a male ...
- Video130.1 MBPublished By ACM
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a single numerical score, e.g., ratings or sentiment scores. This work ...
- Video35.3 MBPublished By ACM
Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors
The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed to combat disinformation either focus on fake news ...
- Video127.2 MBPublished By ACM
Causal Understanding of Fake News Dissemination on Social Media
Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this ...
- Video10:47157.2 MBPublished By ACM
Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementSocial media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing ...
- Video09:31124 MBPublished By ACM
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementAttributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received ...
- Video10.5 MBPublished By ACM
Joint Local and Global Sequence Modeling in Temporal Correlation Networks for Trending Topic Detection
Trending topics represent the topics that are becoming increasingly popular and attract a sudden spike in human attention. Trending topics are critical and useful in modern search engines, which can not only enhance user engagements but also improve ...
- Video5 GBPublished By ACM
Learning From Networks: Algorithms, Theory, and Applications
Arguably, every entity in this universe is networked in one wayr another. With the prevalence of network data collected, such as social media and biological networks, learning from networks has become an essential task in many applications. It is well ...
- Video95:405.5 GBPublished By ACM
Fake News Research: Theories, Detection Strategies, and Open Problems
Fake news has become a global phenomenon due its explosive growth, particularly on social media. The goal of this tutorial is to (1) clearly introduce the concept and characteristics of fake news and how it can be formally differentiated from other ...
- Video18:29364.2 MBPublished By ACM
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised ...
- Video19:38387 MBPublished By ACM
Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods
Feature engineering has found increasing interest in recent years because of its ability to improve the effectiveness of various machine learning models. Although tailored feature engineering methods have been designed for various domains, there are few ...
- Video57:531.1 GBPublished By ACM
Recommendation in social media: recent advances and new frontiers
The pervasive use of social media generates massive data in an unprecedented rate and the information overload problem becomes increasingly severe for social media users. Recommendation has been proven to be effective in mitigating the information ...