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Federated Momentum Contrastive Clustering
Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a ...
Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management
We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum ...
Discovering Expert-Level Air Combat Knowledge via Deep Excitatory-Inhibitory Factorized Reinforcement Learning
- Hai Yin Piao,
- Shengqi Yang,
- Hechang Chen,
- Junnan Li,
- Jin Yu,
- Xuanqi Peng,
- Xin Yang,
- Zhen Yang,
- Zhixiao Sun,
- Yi Chang
Artificial Intelligence (AI) has achieved a wide range of successes in autonomous air combat decision-making recently. Previous research demonstrated that AI-enabled air combat approaches could even acquire beyond human-level capabilities. However, there ...
Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing
Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning ...
Counterfactual Graph Convolutional Learning for Personalized Recommendation
Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced ...
Deep Causal Reasoning for Recommendations
Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a ...
An Explore–Exploit Workload-Bounded Strategy for Rare Event Detection in Massive Energy Sensor Time Series
With the rise of Internet-of-Things devices, the analysis of sensor-generated energy time series data has become increasingly important. This is especially crucial for detecting rare events like unusual electricity usage or water leakages in residential ...
CGKPN: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension
The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding but also ...
Incremental Data Drifting: Evaluation Metrics, Data Generation, and Approach Comparison
Incremental data drifting is a common problem when employing a machine-learning model in industrial applications. The underlying data distribution evolves gradually, e.g., users change their buying preferences on an E-commerce website over time. The ...
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems
Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we ...
Balanced Quality Score: Measuring Popularity Debiasing in Recommendation
Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks ...
Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach
In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users toward personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune ...
Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling
The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby ...
Decentralized Federated Recommendation with Privacy-aware Structured Client-level Graph
Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, ...
Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning
Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into ...
A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation
Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service ...
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel ...
Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional Standards
Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g.,...
Personalized Fashion Recommendations for Diverse Body Shapes with Contrastive Multimodal Cross-Attention Network
Fashion recommendation has become a prominent focus in the realm of online shopping, with various tasks being explored to enhance the customer experience. Recent research has particularly emphasized fashion recommendation based on body shapes, yet a ...
Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation
With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, ...
Trustworthy Recommender Systems
Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an ...
MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games
Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy ...
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent ...
AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation
Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender ...