Multi-Manifold Learning for Large-scale Targeted Advertising System

TL;DR

This paper introduces a multi-manifold hyperbolic embedding framework for targeted advertising, capturing complex hierarchical user-ad relationships to improve prediction accuracy in large-scale systems.

Contribution

It proposes a novel multi-manifold hyperbolic learning approach that models hierarchical structures in user and ad representations for targeted advertising.

Findings

Improved prediction accuracy on benchmark datasets.

Effective modeling of hierarchical user-ad relationships.

Enhanced performance in a large-scale commercial messenger system.

Abstract

Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive them as spam, which eventually leads to a decrease in user satisfaction. Targeted advertising, which serves ads to individuals who may exhibit interest in a particular advertising message, is strongly required. The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space. Most of the previous studies have limited the representation learning in the Euclidean space, but recent studies have suggested hyperbolic manifold learning for the distinct projection of complex network properties emerging from real-world datasets such as social networks, recommender systems, and advertising. We propose a framework that can effectively learn the hierarchical structure in users and ads on the hyperbolic space, and extend to the Multi-Manifold Learning. Our method constructs multiple hyperbolic manifolds with learnable curvatures and maps the representation of user and ad to each manifold. The origin of each manifold is set as the centroid of each user cluster. The user preference for each ad is estimated using the distance between two entities in the hyperbolic space, and the final prediction is determined by aggregating the values calculated from the learned multiple manifolds. We evaluate our method on public benchmark datasets and a large-scale commercial messenger system LINE, and demonstrate its effectiveness through improved performance.