Details

Autor(en) / Beteiligte

• Rahman, Md Mostafizur
• Kikuta, Daisuke
• Abrol, Satyen
• Hirate, Yu
• Suzumura, Toyotaro
• Loyola, Pablo
• Ebisu, Takuma
• Kondapaka, Manoj

Titel

Exploring 360-Degree View of Customers for Lookalike Modeling

Ist Teil von

• Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, p.3400-3404

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2023

Quelle

ACM Digital Library

Beschreibungen/Notizen

• 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.