Exploring 360-Degree View of Customers for Lookalike Modeling
Information Retrieval
2023-04-19 v1 Machine Learning
Abstract
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.
Cite
@article{arxiv.2304.09105,
title = {Exploring 360-Degree View of Customers for Lookalike Modeling},
author = {Md Mostafizur Rahman and Daisuke Kikuta and Satyen Abrol and Yu Hirate and Toyotaro Suzumura and Pablo Loyola and Takuma Ebisu and Manoj Kondapaka},
journal= {arXiv preprint arXiv:2304.09105},
year = {2023}
}