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Found 8 papers, 0 papers with code
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference
Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measurement error problem, which can bias estimation and threaten the validity of inferences.
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i. e., have weakly correlated prediction errors.
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.
Price and Profit Awareness in Recommender Systems
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user.
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Our techniques are motivated by and illustrated on data from a large U. S.
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts.
