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

2025-05-28

AI Summary

Cross-industry co-branding recommendation faces challenges including uncertain partner willingness, resource imbalance, and dynamic market conditions. Method: This paper introduces the first systematic study of co-branding recommendation, proposing an online-offline collaborative bipartite graph modeling framework: nodes represent initiator and target brands, jointly modeling cooperation probability and market revenue; it integrates an exploration-exploitation-balanced online update mechanism with an offline cooperative optimization algorithm aggregating multi-subbrand utilities. Contribution/Results: We theoretically establish a sublinear regret bound and improve the approximation ratio for the NP-hard budget allocation problem. Empirical evaluation on synthetic and real-world datasets demonstrates ≥12% improvement in recommendation performance, significant reduction in early-stage redundant exploration costs, and effective trade-off between short-term returns and long-term strategic growth.

Technology Category

• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Search and Optimization: Combinatorial Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Identifying effective cross-industry co-branding partnerships despite resource imbalances and market uncertainties
• Balancing exploration of new collaborations and exploitation of established ones for optimal gains
• Maximizing overall returns by consolidating sub-brand interests under a parent brand

Innovation

• Unified online-offline co-branding recommendation framework
• Dynamic bipartite graph for brand collaboration probabilities
• Balanced exploration-exploitation for market benefits