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

• URL: http://arxiv.org/abs/2505.22254v1
• Date: Wed, 28 May 2025 11:41:07 GMT
• Title: A Unified Online-Offline Framework for Co-Branding Campaign Recommendations
• Authors: Xiangxiang Dai, Xiaowei Sun, Jinhang Zuo, Xutong Liu, John C. S. Lui,
• Abstract summary: We propose a unified online-offline framework to enable co-branding recommendations.<n>Our approach begins by constructing a bipartite graph linking initiating'' and target'' brands.<n>In the offline optimization phase, our framework consolidates the interests of multiple sub-brands under the same parent brand to maximize overall returns.
• Score: 30.56848329525108
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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