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Evaluative Item-Contrastive Explanations in Rankings
The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the...
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Session-based recommendation with fusion of hypergraph item global and context features
Session-based recommendation (SBR) is to predict the items that users are likely to click afterward by using their recent click history. Learning...
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Item Cold-Start Recommendation with Personalized Feature Selection
The problem of recommending new items to users (often referred to as item cold-start recommendation) remains a challenge due to the absence of users’...
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FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks
To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user...
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Two-view point cloud registration network: feature and geometry
Rigid point cloud registration is a crucial upstream task in computer vision, whose goal is to align two misaligned point clouds using a rigid...
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A Neural Inference of User Social Interest for Item Recommendation
User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive...
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Learning Item Attributes and User Interests for Knowledge Graph Enhanced Recommendation
Knowledge Graphs (KGs) manifest great potential in recommendation. This is ascribable to the rich attribute information contained in KG, such as the...
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Feature selection using max dynamic relevancy and min redundancy
Feature selection algorithms based on three-way interaction information have been widely studied. However, most of these traditional algorithms only...
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How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis
Serendipity is one of beyond-accuracy objectives for recommender systems (RSs), which aims to achieve both relevance and unexpectedness of...
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Feature Engineering
In this chapter, we will introduce to you the key part in the algorithm competition that has the heaviest workload and that determines whether the...
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An Adaptive Feature Selection Method for Learning-to-Enumerate Problem
In this paper, we propose a method for quickly finding a given number of instances of a target class from a fixed data set. We assume that we have a...
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Swin-LBP: a competitive feature engineering model for urine sediment classification
Automated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract...
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A review on emotion recognition from dialect speech using feature optimization and classification techniques
Emotion recognition from speech has gained prominence across various domains due to its wide-ranging applications. This paper presents a...
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Improving small object detection via context-aware and feature-enhanced plug-and-play modules
Detecting small objects is a challenging task in computer vision due to the objects only occupying a limited number of pixels and having blurred...
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Multi-source deep transfer learning algorithm based on feature alignment
With the deepening of transfer learning research, researchers are no longer satisfied with the classification of knowledge in a single field but hope...
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Feature selection using three-stage heuristic measures based on mutual fuzzy granularities
Mutual information is fundamental for feature selection, and relevant conditional and joint mutual fuzzy granularities (MFGs) characterize feature...
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A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm
Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features....
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Mitigating the curse of dimensionality using feature projection techniques on electroencephalography datasets: an empirical review
Electroencephalography (EEG) is commonly employed to diagnose and monitor brain disorders, however, manual analysis is time-consuming. Hence,...
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Research on Feature Selection Algorithm of Energy Curve
Energy analysis attack is a side channel attack, which collects and analyzes the power leakage information in the operation process of cryptographic...
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A NOx emission prediction hybrid method based on boiler data feature subset selection
Simplicity, efficiency and precision are basic principles for modeling and analyzing of coal-fired boilers data. However, the load fluctuations,...
