Learning to rank for uplift modeling

02/14/2020
by   Floris Devriendt, et al.
0

Uplift modeling has effectively been used in fields such as marketing and customer retention, to target those customers that are most likely to respond due to the campaign or treatment. Uplift models produce uplift scores which are then used to essentially create a ranking. We instead investigate to learn to rank directly by looking into the potential of learning-to-rank techniques in the context of uplift modeling. We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework. Additionally, we introduce a new metric for learning-to-rank that focusses on optimizing the area under the uplift curve called the promoted cumulative gain (PCG). We employ the learning-to-rank technique LambdaMART to optimize the ranking according to PCG and show improved results over standard learning-to-rank metrics and equal to improved results when compared with state-of-the-art uplift modeling. Finally, we show how learning-to-rank models can learn to optimize a certain targeting depth, however, these results do not generalize on the test set.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

05/04/2022

Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain

Learning-to-rank, a machine learning technique widely used in informatio...
08/31/2020

Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics

Learning-to-rank has been intensively studied and has shown significantl...
04/04/2022

Which Tricks are Important for Learning to Rank?

Nowadays, state-of-the-art learning-to-rank (LTR) methods are based on g...
05/23/2017

Hashing as Tie-Aware Learning to Rank

Hashing, or learning binary embeddings of data, is frequently used in ne...
09/19/2019

ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking

In this work we describe the system from Natural Language Processing gro...
04/30/2018

Counterfactual Learning-to-Rank for Additive Metrics and Deep Models

Implicit feedback (e.g., clicks, dwell times) is an attractive source of...
01/21/2021

Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval

Our work aimed at experimentally assessing the benefits of model ensembl...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.