The system recommends hotels based on user preferences.
problem Overwhelm of hotel choices during trip planning.
method Used Expedia's hotel dataset to predict user preferences.
result Predicted and recommended hotels with high accuracy.
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arXiv research
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48 results for “hotelling observer”
Dataset of 50k hotels aids in human trafficking investigations.
problem Identify hotels from low-quality, occluded images.
method Curated dataset of 1M annotated hotel room images, baseline model with data augmentation.
result Demonstrated the feasibility of hotel recognition from challenging images.
This research uses machine learning to approximate ideal and hotelling observer performance for binary signal detection.
problem Approximating the Ideal and Hotelling Observers for binary signal detection tasks.
method Supervised learning methods, including CNNs and SLNNs, are employed to approximate the IO and HO test statistics.
result The proposed supervised learning methods provide accurate approximations of the IO and HO test statistics.
Hotel2vec learns hotel embeddings from multiple data sources.
problem Cold-start problem for hotels with insufficient click data.
method Self-supervised neural network architecture combining user clicks, hotel attributes, and geographic info.
result Improved downstream task predictions with structured hotel attributes.
PriceAggregator optimizes hotel price fetching to increase Agoda's bookings.
problem Limited QPS from suppliers causes many user searches to be ignored.
method Intelligently determines queries to suppliers for price fetching.
result PriceAggregator increases Agoda's bookings significantly.
Hotel booking chatbot handles daily tens of thousands of searches.
problem Improving hotel booking through conversational AI.
method Frame-based dialogue management system with ML models for intent, entity recognition, and info retrieval.
result Chatbot deployed on a commercial scale, handling hotel searches daily.
Researchers solved a model of an exhaustible resource with stochastic discoveries.
problem Optimal exploration of an exhaustible resource with uncertain discoveries.
method Impulse control and Poisson process of new discoveries.
result A frontier of critical levels of proven reserves exists, above which exploration is stopped.
Study analyzes Hotelling-type tensor deflation for spiked tensors, providing insights into signal and noise.
problem Characterizing singular values and alignments in Hotelling-type tensor deflation.
method Asymptotic study of Hotelling-type tensor deflation in large dimensional regime using random tensor theory.
result Characterization of singular values and alignments at each step of the deflation procedure.
A method uses CG to create efficient channels for ideal observers.
problem Computational intractability of ideal observers for high-dimensional image data.
method Conjugate gradient (CG) method for constructing efficient channels.
result CG-based channels approximate IO and HO performance efficiently.
New methods explain NE embeddings by identifying key variables.
problem Lack of interpretability in NE techniques.
method Combining PCA, Q-residuals, Hotelling's T2, and visualization.
result Identifies discriminatory features not seen in standard approaches.
Study suggests variable selection may not significantly reduce power in multivariate tests.
problem The feasibility of parsimonious variable selection in Hotelling's T2-test.
method Investigation of power loss when selecting small subsets of variables from multivariate data.
result Some evidence suggests no significant power loss over a wide range of alternatives.
Study analyzes accuracy of tensor deflation in noisy conditions.
problem Analyzing accuracy of tensor deflation in noisy conditions.
method Asymptotic study of Hotelling-type tensor deflation in large tensor dimensions.
result Characterization of estimated singular values and singular vector alignments.
This paper analyzes how errors accumulate in PCA's deflation method.
problem Error accumulation in PCA's deflation method.
method Mathematical analysis of inexact Hotelling's deflation method in two scenarios.
result Characterization of error propagation in PCA's deflation method.
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.
H. Hotelling proved that in the n-dimensional Euclidean or spherical space, the volume of a tube of small radius about a curve depends only on the length of the curve and the radius. A. Gray and L. Vanhecke extended Hotelling's theorem to rank one symmetric spaces computing the volumes of the tubes explicitly in these …
Many-to-one RNN predicts user hotel clicks from browsing history.
problem Predicting user hotel clicks from browsing history.
method Combines rule-based algorithm with Gated RNN to sort accommodations.
result Promising results but computationally demanding.
Rethinking value creation from the resource based view: the case of human capital in moroccan hotelscs.CY
The growth of the modern knowledge-based economy is becoming less and less dependent on tangible assets and more on intangible ones. In this context, the role of human capital in the value creation process has become central. Despite the large amount of scientific work on human capital phenomena, little research has re…
Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical correlation analysis breaks down. We propose a sparse canonical correlation analysis by adding l1 constraints on the canonical vec…
New algorithm speeds up fair clustering by 12x.
problem Achieving fair clustering in scalable algorithms.
method s-FairSC algorithm incorporating nullspace projection and Hotelling's deflation.
result s-FairSC is 12x faster and maintains fairness.
A new algorithm improves efficiency and robustness of heuristic optimization in simulation-based problems.
problem Optimizing input parameters for stochastic simulation-based optimization.
method Reactive sample size algorithm based on parametric tests and indifference-zone selection.
result The reactive method improves efficiency and robustness of heuristic optimization techniques.
When data analysts train a classifier and check if its accuracy is significantly different from chance, they are implicitly performing a two-sample test. We investigate the statistical properties of this flexible approach in the high-dimensional setting. We prove two results that hold for all classifiers in any dimensi…
Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human activities. In this article, methods of treating complexity arising from a vast amount of …
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD based K-chart was first introduced by Sun and Tsung for monitoring multivariate processes when underlying distribution of process parameters or quality characteristics depart from Norm…
SIMPLE method quantifies uncertainty in network membership profiles.
problem Quantifying statistical uncertainty in network membership profiles.
method Degree-corrected mixed membership model, Hotelling-type statistics.
result Established exact limiting distributions of test statistics.
The paper analyzes deflation for estimating a low-rank spike in large tensors with noise.
problem Estimating a low-rank symmetric spike in large tensors with additive Gaussian noise.
method Characterization of deflation performance in terms of vector alignments and weights.
result Understanding deflation mechanism in noisy conditions and designing more efficient methods.
A new RL method improves revenue management with delayed feedback.
problem Delayed feedback in revenue management affects substantial value.
method Choice-model-assisted Q-learning for delayed feedback revenue management.
result Q-learning with model-imputed targets converges to an optimal Q-function.
We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label…
Generative model learns object variability from MRI measurements.
problem Establishing stochastic object models from medical imaging data.
method Advanced AmbientGANs with multiresolution training.
result AmbientGANs reliably learn object distributions from incomplete or noisy data.
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research interest. IVA has also been shown to be a generalization of Hotelling's canonical correlation analysis. In this paper, we provide the ident…
The asymptotic distribution of the Markowitz portfolio is derived, for the general case (assuming fourth moments of returns exist), and for the case of multivariate normal returns. The derivation allows for inference which is robust to heteroskedasticity and autocorrelation of moments up to order four. As a side effect…
We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional setting, allowing for the data dimension p to exceed the sample size n. Specifically, we propose a new test statistic for the two-sample test of means that integrates a rand…
Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, measure, AUC, and so on. In multivariate tests, comparison is done using multiple measures simultaneously. For example, error is the sum of false positives and false negatives…
The paper identifies universal features for high-dimensional data inference.
problem Identifying universal low-dimensional features from high-dimensional data for inference tasks.
method Introduces natural notions of universality and shows a local equivalence among them, using information geometry.
result Reveals the complementary roles of various data analysis techniques.
Paper presents a new framework for optimal asset and signal combination.
problem Optimal asset and signal combination problem.
method Two-stage approach: reformulate dynamic portfolio selection problem, then use Canonical Correlation Analysis.
result Improved performance of proposed method over natural benchmarks.
Paper presents a privacy-preserving method for dynamic assortment selection.
problem Personalized assortment recommendations with data privacy concerns.
method Perturbed upper confidence bound method integrating calibrated noise.
result Policy satisfies Joint Differential Privacy (JDP) with near-optimal regret bound.
Explicit Taylor series for the volume of tubes in Lie groups
problem Computing the volume of tubes in riemannian manifolds
method Using bi-invariant metrics
result Explicit Taylor series for the volume of a tube in a Lie group
It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural …
Paper proposes a new KPI for early fault detection in hydropower plants.
problem Early detection and maintenance of faults in hydropower plants.
method Developed and tested a novel Key Performance Indicator (KPI).
result The KPI outperforms conventional multivariable process control charts.
Study optimizes pricing under uncertainty and capacity constraints.
problem Optimizing pricing decisions under demand uncertainty and capacity constraints.
method Analyzes linear demand, stochastic noise, and finite capacity; uses certified demand forecasts and control variates.
result Certified demand forecasts reduce regret from to under certain conditions.
New method detects bearing faults using multivariate statistical process control.
problem Early detection of bearing faults in rotating machinery.
method Multivariate statistical process control charts applied to Fourier transform features of fixed-time batches.
result Effectiveness in detecting bearing faults across different conditions.
AI classifies tourist events for better service.
problem Classifying tourist events efficiently across diverse sources.
method CRISP-DM, supervised machine learning, NLP.
result Automatic classification tool for consistent event catalogs.
Paper tackles dynamic assortment with dual contexts, improving revenue in e-commerce.
problem Maximizing revenue in e-commerce with personalized recommendations from vast catalogs.
method Low-rank dynamic assortment model and upper confidence bound approach.
result Regret bound of for dynamic assortment problem.
FRONT optimizes decisions with interference, reducing regret over time.
problem Short-sighted policies in online decision-making due to ignoring interference.
method FRONT considers long-term impacts of decisions, using exploratory and exploitative strategies.
result FRONT achieves sublinear regret in both immediate and consequential impacts.
This paper considers the problem of canonical-correlation analysis (CCA) (Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a pair of symmetric matrices. These are two fundamental problems in data analysis and scientific computing with numerous applications in machine learning and statistics (…
We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources, and returns different rewards for the resources consumed. We consid…
Nonparametric two sample testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. We refer to the most common settings as mean difference alternatives (MDA), for testing differences only in firs…
Generative model for hypergraph clustering improves detection of higher-order structure.
problem Detecting clusters in complex relational systems modeled as hypergraphs.
method Poisson degree-corrected hypergraph stochastic blockmodel (DCHSBM) and Louvain-type algorithms.
result AON hypergraph Louvain algorithm efficiently detects higher-order structure in large hypergraphs.
Paper identifies unobserved variables from observable data.
problem Missing variables in empirical studies.
method Function mapping from observables to unobservables based on joint distribution.
result Uniqueness of latent values in each observation.