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View article: Detecting Routines: Applications to Ridesharing Customer Relationship Management
Detecting Routines: Applications to Ridesharing Customer Relationship Management Open
Routines shape many aspects of day-to-day consumption. While prior research has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which the authors define as rep…
View article: Replication Data for: 'Overcoming the Cold Start Problem of Customer Relationship Management Using a Probabilistic Machine Learning Approach'
Replication Data for: 'Overcoming the Cold Start Problem of Customer Relationship Management Using a Probabilistic Machine Learning Approach' Open
This dataset includes the code for the simulation section that generates the data, the stan model, the estimation, and the post-estimation codes to replicate results in “Overcoming the Cold Start Problem of Customer Relationship Management…
View article: Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT)
Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT) Open
Significance Decision makers now use algorithmic personalization for resource allocation decisions in many domains (e.g., medical treatments, hiring decisions, product recommendations, or dynamic pricing). An inherent risk of personalizati…
View article: Replication Data for 'Eliminating Unintended Bias in Personalized Policies Using Bias Eliminating Adapted Trees (BEAT)'
Replication Data for 'Eliminating Unintended Bias in Personalized Policies Using Bias Eliminating Adapted Trees (BEAT)' Open
This dataset includes the files (code and data) needed to replicate the results in “Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT).” The R code replicates the simulation analyses and result…