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

arXiv: https://arxiv.org/abs/2505.22254 · HTML: https://mlsanity.com/paper/2505.22254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 13 of 13)

- [Improved Optimistic Algorithms for Logistic Bandits](https://mlsanity.com/paper/2002.07530/) (2020)
- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications](https://mlsanity.com/paper/1703.01610/) (2017)
- [Cost-Effective Online Multi-LLM Selection with Versatile Reward Models](https://mlsanity.com/paper/2405.16587/) (2024)
- [Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation](https://mlsanity.com/paper/2307.04571/) (2023)
- [Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms](https://mlsanity.com/paper/2208.14837/) (2022)
- [Dual-Mandate Patrols: Multi-Armed Bandits for Green Security](https://mlsanity.com/paper/2009.06560/) (2020)
- [Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits](https://mlsanity.com/paper/2305.12402/) (2023)
- [Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning](https://mlsanity.com/paper/2106.05065/) (2021)
- [Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals](https://mlsanity.com/paper/2306.07071/) (2023)
- [Conversational Dueling Bandits in Generalized Linear Models](https://mlsanity.com/paper/2407.18488/) (2024)
- [Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts](https://mlsanity.com/paper/2501.00891/) (2025)

## Built on by (most cited first, 1 of 1)

- [Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation](https://mlsanity.com/paper/2508.07675/) (2025)

## Abstract (arXiv)

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.