Identifies useful product reviews from online consumer feedback.
problem Finding useful reviews among noisy consumer feedback.
method Explores latent semantic factors in reviews using HMM-LDA model.
result Significant improvement in predicting useful reviews over baselines.
Deep learning predicts mismatched ratings in Amazon reviews.
problem Identifying reviews with mismatched ratings on Amazon.
method Converted reviews to vectors using paragraph vector, trained a recurrent neural network with gated recurrent unit, incorporated semantic relationships.
result Model accurately predicts rating mismatches and provides feedback.
Paper tackles non-credible reviews by deriving consistency features from limited data.
problem Identifying credible online reviews amidst fake, incompetent, and biased reviews.
method Uses latent topic models to derive consistency features from review texts, item ratings, and timestamps.
result Improves credibility detection over state-of-the-art baselines on real-world datasets.
Experiment shows author rankings can improve peer review scores.
problem Improving accuracy in machine learning conference peer review.
method Used Isotonic Mechanism to calibrate review scores using author rankings.
result Calibrated scores outperform raw scores in estimating ground truth review scores.
NIPS 2016 analyzed its review process to improve future conferences.
problem Growth in submissions, reviewers, and attendees requires better quality assessment.
method Analyzed data from the review process, including ordinal rankings experiments.
result Investigated the efficacy of collecting ordinal rankings from reviewers.
The paper proposes a rubric and incentives to prevent peer review from becoming a 'tragedy of the commons'.
problem The growth of machine learning threatens the sustainability of peer review.
method Proposes a rubric for objective review quality and financial compensation for reviewers.
result Avoiding a 'tragedy of the commons' outcome in peer review.
Paper uses pre-trained models and active learning to analyze customer reviews quickly.
problem Automatic review analysis with limited labeled data and time.
method Pre-trained language representation and active learning framework.
result Fully automatic review analysis achieved at a faster pace.
Unsupervised summarization generates novel reviews reflecting consensus opinions.
problem Creating summaries that reflect subjective information in multiple documents.
method Generative model with hierarchical variational autoencoder, pointer-generator mechanism.
result Model produces fluent and coherent summaries reflecting common opinions.
Paper detects review abuse using tensor decomposition.
problem Detecting review abuse by sellers and reviewers.
method Semi-supervised binary multi-target tensor decomposition.
result The model achieves higher precision and recall.
Algorithm maximizes review quality of least advantaged paper and ensures accurate paper acceptance.
problem Fair and accurate assignment of papers to reviewers in peer review.
method Incremental max-flow procedure for fairness, minimax analysis for accuracy.
result Near-optimal fairness and accuracy of paper assignment.
ScoreGAN uses GANs with IGM to detect bot-generated reviews based on text and scores.
problem Lack of labeled data and bot-generated reviews in fraud review detection.
method ScoreGAN incorporates review text and scores into a GAN framework for data augmentation and detection.
result ScoreGAN outperforms existing methods by 7% and 5% on Yelp and TripAdvisor datasets.
Mathematical general relativity reviewed.
problem Challenges in mathematical modeling of general relativity.
method Selected topics reviewed.
result Insights into mathematical models of general relativity.
Paper quarantines unreliable Yelp users by detecting review spam.
problem Unreliable and spamming users deceive Yelp's users.
method Used RSD and spam detection techniques on key features.
result More than 80% of Yelp's accounts are unreliable, and highly-rated businesses are often spammed.
The paper tackles strategic review issues in conference settings.
problem Strategic reviews by reviewers who are also authors can bias the ranking of papers.
method The authors present a strategyproof and efficient peer review algorithm based on partitioning.
result The algorithm guarantees strategyproofness and unanimity under certain conditions.
The paper analyzes e-commerce reviews using RNN for sentiment classification.
problem Understanding customer sentiments in e-commerce reviews.
method Univariate and multivariate analyses on dataset features except review texts. Bidirectional RNN with LSTM implemented for classification.
result Bidirectional LSTM achieved high F1-scores for recommendation and sentiment classification.
This study analyzes app reviews to understand students' behavior in the app market.
problem Extracting sentiment from growing app reviews manually is impractical.
method Used machine learning algorithms with TF-IDF for text representation and ensemble learning for evaluation.
result SVM achieved the highest accuracy (93.37%) on tri-gram + TF-IDF scheme.
RevGAN generates personalized reviews with given sentiment and style.
problem Creating reviews that are both personalized and of high quality.
method RevGAN combines self-attentive recursive autoencoders, conditional discriminators, and personalized decoders.
result RevGAN outperforms state-of-the-art models in sentence quality, coherence, and personalization.
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
Proposes RTL model for sentiment classification and key word detection in online reviews.
problem Sentiment classification and key word detection in online reviews for hospitality industry.
method Regularized Text Logistic (RTL) regression model.
result RTL model achieves satisfactory classification performance and identifies key word features.
Machine learning analyzed peer reviews to find differences in quality by journal impact factor.
problem Determining if higher journal impact factors correlate with more thorough or helpful peer reviews.
method Hand-coded and machine-learned analysis of 10,000 peer review sentences from 1,644 journals.
result Peer reviews in higher impact factor journals are more thorough in discussing methods but less helpful in suggesting solutions and providing examples.
Paper proposes spamGAN to detect and generate opinion spam using limited labeled data.
problem Detecting and preventing opinion spam in online reviews with limited labeled data.
method Generative adversarial network (GAN) trained on semi-supervised data.
result spamGAN outperforms existing techniques in detecting opinion spam with limited labeled data.
Review of conformal geometry in irrational rotation algebra.
problem Understanding conformal geometry of irrational rotation algebra.
method Review of recent progress by Connes and others.
result Recent progress in understanding conformal geometry.
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
Neural networks reviewed for option pricing and hedging.
problem Improving option pricing and hedging models using neural networks.
method Comparison of over 100 papers on neural networks for option pricing and hedging.
result Papers compared on various aspects including input features, output variables, and performance measures.
This paper reviews sentiment analysis on Indian languages.
problem Understanding sentiment in multilingual web data.
method Reviews and discusses approaches for sentiment analysis on Indian languages.
result Challenges in sentiment analysis on indigenous languages.
This paper is a review of the book "Knots" by Alexei Sossinsky. The review includes a short personal history of knot theory at the end of the twentieth century.
Recommender system improves with temporal representations.
problem Improving interpretability and performance in recommender systems.
method Incorporates temporal representations via recurrent point process in continuous time.
result Characterizes effects of perception, interest, and seasonal changes on reviews.
This review classifies deep generative models from a graphical modeling perspective.
problem Learning with deep generative models from a graphical modeling perspective.
method Organized from graphical modeling perspective, differentiating model definitions from learning algorithms.
result Different learning algorithms can be applied to the same model.
This review examines deep learning in financial fraud detection over 5 years.
problem Improving deep learning techniques for financial fraud detection.
method Systematic literature review of 57 studies using performance metrics.
result Deep learning models enhance fraud detection across various financial domains.
Improved collaborative filtering with neural network models of reviews.
problem Improve collaborative filtering performance using side information from reviews.
method Introduced two neural network models (product-of-experts and recurrent neural network) to incorporate reviews into collaborative filtering.
result The product-of-experts model achieved state-of-the-art performance, outperforming LDA-based approach.
Living review of ML for particle physics, updated frequently.
problem Keeping up with rapid advancements in ML for particle physics.
method Creating a living document to list and update citations of ML applications.
result Provides a comprehensive list of ML citations for particle physics.
Review of financial market data clustering and networks.
problem Understanding correlations, hierarchies, and networks in financial markets.
method Compilation and synthesis of research from various fields.
result A comprehensive resource for financial market analysis.
This paper reviews Douglas curvature in Finsler geometry.
problem Exploring Douglas curvature in Finsler spaces.
method Historical review, characterizations, generalizations, and applications.
result Significance and applications of Douglas curvature in Finsler geometry.
LLM extracts actionable insights from customer reviews.
problem Extracting actionable insights from customer reviews.
method Large language model approach distinguishing perceptual attributes from actionable features.
result High consistency and predictive validity of LLM insights compared to human coders.
Meta-analysis and systematic review can help distill machine learning research.
problem Difficulties in distilling a flood of machine learning papers into useful principles.
method Use meta-analysis and systematic review to aggregate and analyze machine learning research.
result Meta-analysis and systematic review can help in distilling machine learning research.
Online reviews predict long-term stock returns.
problem Little research on predicting individual stock returns using online reviews.
method Selected 6,246 features from 18 million reviews; built prediction models with cross-validation.
result Achieved 13.94% accuracy improvement over existing methods.
Review of deep learning methods in medical image registration.
problem Improving accuracy and efficiency of medical image registration.
method Classification and detailed analysis of seven categories of DL-based registration methods.
result Comprehensive comparison of DL-based methods for lung and brain registration.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine learning.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
Review of Yau's conjecture on zero sets of Laplace eigenfunctions.
problem Yau's conjecture on zero sets of Laplace eigenfunctions.
method Discussion of old and new results and methods related to the conjecture, including solutions and new results in smooth settings.
result Discussion of Donnelly and Fefferman's solution of the conjecture in the real-analytic Riemannian manifold case and new results in the smooth setting.
Survey on statistical theories of neural networks, focusing on approximation, training dynamics, and generative models.
problem Understanding the statistical properties and training dynamics of neural networks.
method Review of existing literature on neural networks from three perspectives: approximation, training dynamics, and generative models.
result Theoretical insights into neural network training dynamics and generative models.
This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.
problem Lack of systematic review in digital transformation from corporate finance perspective.
method Combines bibliometric and content analysis methods.
result Emerging and rapidly growing focus on digital transformation, particularly in developed countries.
Graph Neural Networks improve financial fraud detection.
problem Complex financial transactions pose challenges in fraud detection.
method Unified framework of GNN methodologies applied to financial fraud detection.
result GNNs excel at capturing complex relational patterns in financial networks.
This paper reviews quantum machine learning from NISQ to fault tolerance.
problem The challenges and opportunities in quantum machine learning.
method Comprehensive review of quantum machine learning concepts.
result Coverage of NISQ and fault-tolerant quantum computing approaches.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
Quantum invariants of 3-manifolds and links reviewed, with connections to other topological invariants.
problem Quantum invariants of 3-manifolds and links.
method Review of recent developments and connections to other invariants.
result Rich features of quantum invariants like quantum modularity and Verma module structures.
A concise review of recent few-shot meta-learning methods.
problem Mimicking human fast adaptation to new concepts based on prior knowledge.
method Categorized into four branches based on technical characteristics.
result Current challenges and future prospects identified.
Survey reviews machine learning for automatic movement generation.
problem Need for automatic movement animation in interactive media.
method Machine learning techniques and motion capture data.
result Discussion of research gaps and challenges for future work.