A Unified Online-Offline Framework for Co-Branding Campaign Recommendations

TL;DR

This paper introduces a comprehensive online-offline framework for co-branding recommendations that dynamically adapts to market feedback, optimizes partnerships, and improves overall returns with proven theoretical guarantees and practical effectiveness.

Contribution

It is the first systematic study proposing a unified framework combining online learning and offline optimization for co-branding, addressing resource and market uncertainties.

Findings

Achieves at least 12% improvement in co-branding success metrics.

Provides a sublinear regret bound for online learning in co-branding.

Enhances offline budget allocation guarantees for complex scenarios.

Abstract

Co-branding has become a vital strategy for businesses aiming to expand market reach within recommendation systems. However, identifying effective cross-industry partnerships remains challenging due to resource imbalances, uncertain brand willingness, and ever-changing market conditions. In this paper, we provide the first systematic study of this problem and propose a unified online-offline framework to enable co-branding recommendations. Our approach begins by constructing a bipartite graph linking ``initiating'' and ``target'' brands to quantify co-branding probabilities and assess market benefits. During the online learning phase, we dynamically update the graph in response to market feedback, while striking a balance between exploring new collaborations for long-term gains and exploiting established partnerships for immediate benefits. To address the high initial co-branding costs, our framework mitigates redundant exploration, thereby enhancing short-term performance while ensuring sustainable strategic growth. In the offline optimization phase, our framework consolidates the interests of multiple sub-brands under the same parent brand to maximize overall returns, avoid excessive investment in single sub-brands, and reduce unnecessary costs associated with over-prioritizing a single sub-brand. We present a theoretical analysis of our approach, establishing a highly nontrivial sublinear regret bound for online learning in the complex co-branding problem, and enhancing the approximation guarantee for the NP-hard offline budget allocation optimization. Experiments on both synthetic and real-world co-branding datasets demonstrate the practical effectiveness of our framework, with at least 12\% improvement.