# CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation

arXiv: https://arxiv.org/abs/2505.03840 · HTML: https://mlsanity.com/paper/2505.03840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 12 of 12)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays](https://mlsanity.com/paper/1506.00779/) (2015)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests](https://mlsanity.com/paper/2003.00359/) (2020)
- [Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms](https://mlsanity.com/paper/1708.03058/) (2017)
- [Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation](https://mlsanity.com/paper/2008.09368/) (2020)
- [Combinatorial Bandits under Strategic Manipulations](https://mlsanity.com/paper/2102.12722/) (2021)
- [Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting](https://mlsanity.com/paper/2001.08655/) (2020)
- [A Hybrid Bandit Framework for Diversified Recommendation](https://mlsanity.com/paper/2012.13245/) (2020)
- [The Impact of Situation Clustering in Contextual-Bandit Algorithm for Context-Aware Recommender Systems](https://mlsanity.com/paper/1304.3845/) (2013)

## Abstract (arXiv)

Clustering bandits have gained significant attention in recommender systems by leveraging collaborative information from neighboring users to better capture target user preferences. However, these methods often lack a clear definition of similar users and face challenges when users with unique preferences lack appropriate neighbors. In such cases, relying on divergent preferences of misidentified neighbors can degrade recommendation quality. To address these limitations, this paper proposes an adaptive Collaborative Combinatorial Bandits algorithm (CoCoB). CoCoB employs an innovative two-sided bandit architecture, applying bandit principles to both the user and item sides. The user-bandit employs an enhanced Bayesian model to explore user similarity, identifying neighbors based on a similarity probability threshold. The item-bandit treats items as arms, generating diverse recommendations informed by the user-bandit's output. CoCoB dynamically adapts, leveraging neighbor preferences when available or focusing solely on the target user otherwise. Regret analysis under a linear contextual bandit setting and experiments on three real-world datasets demonstrate CoCoB's effectiveness, achieving an average 2.4% improvement in F1 score over state-of-the-art methods.