On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,291 papers · 148 categories

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14 results for “clickstream”

Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Modelsstat.ME

A new mixture model approach for clickstream data combines unsupervised and semi-supervised learning.

problem Lack of statistical learning approaches for clickstream data.

method Finite mixture models of first-order continuous time Markov models.

result The approach effectively handles the time spent on webpages, improving classification accuracy.

clickstream data unsupervised learning semi-supervised classification Markov models continuous time

2018-02-13abs ↗pdf ↗

ChoiceRank: Identifying Preferences from Node Traffic in Networksstat.ML

ChoiceRank learns network edge probabilities from node traffic data.

problem Learning edge transition probabilities from node-level traffic data.

method Preference learning model based on Luce's axiom, iterative algorithm.

result Successfully recovers edge transition probabilities from marginal node traffic data.

network traffic edge probabilities clickstream mobility networks Luce's axiom

2016-10-20abs ↗pdf ↗

Dropout Model Evaluation in MOOCsstat.AP

Study shows better dropout prediction from clickstream data in MOOCs.

problem Evaluating predictive models of student success in MOOCs.

method Statistical testing of hypotheses about model performance, focusing on algorithms and feature extraction methods.

result Clickstream-based feature extraction outperforms forum- and assignment-based methods in predicting student dropout.

MOOCs dropout prediction feature extraction statistical testing predictive models

2018-02-16abs ↗pdf ↗

Scalable bundling via dense product embeddingscs.LG

New method uses product embeddings to predict bundle success.

problem Designing effective product bundles in large retail settings.

method Leverage historical purchases and clickstream data to generate product embeddings, then use heuristics for complementarity and substitutability.

result Embeddings-based heuristics predict bundle success, robust across categories and retailers.

product bundling machine learning dense embeddings retail clickstream data

2020-01-31abs ↗pdf ↗

Root Cause Detection Among Anomalous Time Series Using Temporal State Alignmentcs.LG

The paper proposes a method to identify root causes of anomalies in time series data.

problem Challenges in identifying the root cause of anomalies in complex software systems.

method Analyzes time series fluctuations to track the propagation of effects through hidden states.

result Identifies causal patterns among observed fluctuations to isolate root causes.

anomaly detection root cause analysis time series analysis hidden states Zillow clickstream data

2020-01-04abs ↗pdf ↗

One Embedding To Do Them Allcs.LG

Unified product embeddings improve cross-task performance in e-commerce.

problem Training product embeddings in isolation limits cross-task performance.

method Combining text, clickstream, and image data using denoising auto-encoders, BPR, and Siamese neural networks.

result Unified product embeddings uniformly outperform isolated embeddings across three e-commerce tasks.

e-commerce product embeddings cross-task performance unified models denoising auto-encoders

2019-06-28abs ↗pdf ↗

Rational Kernels: A surveycs.LG

Rational kernels offer a way to handle sequence data efficiently.

problem Handling sequence data with fixed-length feature vectors is cumbersome.

method Rational kernels provide an elegant representation for sequences.

result Rational kernels offer a convenient way to use sequence data.

rational kernels sequence data fixed-length feature vectors kernel functions clickstream data

2019-10-20abs ↗pdf ↗

Distributed Coordinate Descent for Generalized Linear Models with Regularizationstat.ML

Distributed algorithm for fitting generalized linear models with regularization.

problem Training large-scale generalized linear models with regularization in distributed environments.

method Distributed coordinate descent with line search, addressing slow node problem.

result Our approach is scalable and superior for training on large and sparse datasets.

distributed computing coordinate descent generalized linear models regularization logistic regression

2016-11-07abs ↗pdf ↗

Attributed Sequence Embeddingcs.LG

Paper tackles embedding attributed sequences in unsupervised learning.

problem Mining tasks over attributed sequences with dependencies between sequences and attributes.

method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.

result NAS produces task-independent embeddings for various mining tasks on real-world datasets.

attributed sequence embedding unsupervised learning deep multimodal learning gene sequences user behavior analysis

2019-11-03abs ↗pdf ↗

Transfer Learning using Representation Learning in Massive Open Online Coursescs.LG

Improved transfer learning for MOOCs using auto-encoders.

problem Poor prediction performance of models trained on historical MOOC data.

method Automated transductive transfer learning with auto-encoders.

result Improved transferability of dropout prediction across similar and dissimilar MOOCs.

MOOCs Transfer Learning Auto-encoders Representation Learning Predictive Models

2018-12-12abs ↗pdf ↗

Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data streamcs.LG

Estimates customer segments from continuous marketing data streams.

problem Analyzing large, continuously updated marketing data streams.

method oFMLR: online estimation of finite mixture of logistic regression models.

result oFMLR provides interpretable customer segment clustering.

maximum likelihood logistic regression customer segmentation streaming data online estimation

2018-02-28abs ↗pdf ↗

Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphscs.LG

A new method recovers latent potentials from graph flows, preserving ordering and stability.

problem Recovering latent potentials from graph flows is ill-posed and standard methods collapse the ordering.

method Gauge-invariant, parameter-insensitive regularization using Dirichlet energy.

result The method preserves ordering and stability across different regularization strengths.

graph learning inverse problems graph neural networks gauge invariance parameter insensitivity

2026-07-15abs ↗pdf ↗

Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Coursescs.LG

Enhances MOOC learning models with unsupervised feature learning.

problem Designing effective features for personalized MOOC interventions.

method Unsupervised learning using a modified auto-encoder (AE) combined with LSTM network.

result Improved prediction accuracy by up to 17% compared to supervised models.

MOOCs unsupervised learning feature learning predictive models long short-term memory (LSTM)

2018-12-12abs ↗pdf ↗

Machine Learning for Temporal Data in Finance: Challenges and Opportunitiesq-fin.ST

Machine learning struggles with temporal data in finance, leading to inaccurate models.

problem Machine learning models struggle with temporal data in finance, leading to inaccurate predictions.

method Review and critique current machine learning approaches for temporal data in finance.

result Current approaches to machine learning in finance often ignore the temporal richness of data.

machine learning finance temporal data temporal patterns financial services

2020-09-11abs ↗pdf ↗