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Using Word Embedding to Reveal Monetary Policy Explanation Changes
Documents have been an essential tool of communication for governments to announce their policy operations.
Word Embedding for Social Sciences: An Interdisciplinary Survey
To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode.
Extracting Fast and Slow: User-Action Embedding with Inter-temporal Information
We simultaneously embed the user's action sequence and its time intervals to obtain a low-dimensional representation of the action along with intertemporal information.
A Real-World Implementation of Unbiased Lift-based Bidding System
Second, thepractical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact. We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules.
Online-to-Offline Advertisements as Field Experiments
Herein, we study the difference in offline behavior between customers who received online advertisements and regular customers (i. e., the customers visits the target shop voluntary), and the duration of this difference.
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms
In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories.
Unbiased Lift-based Bidding System
Rather, the bidding strategy that leads to the maximum revenue is a strategy pursuing the performance lift of showing ads to a specific user.
Detecting multi-timescale consumption patterns from receipt data: A non-negative tensor factorization approach
Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies.
