CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation
Cairong Yan, Jinyi Han, Jin Ju, Yanting Zhang, Zijian Wang, Xuan Shao

TL;DR
CoCoB introduces an adaptive collaborative bandit algorithm for online recommendations that dynamically balances neighbor influence and individual preferences, improving recommendation accuracy.
Contribution
This paper presents a novel two-sided bandit architecture with adaptive neighbor selection, enhancing collaborative filtering in recommender systems.
Findings
Achieves 2.4% higher F1 score than state-of-the-art methods.
Effectively identifies user neighbors using a Bayesian similarity model.
Demonstrates robustness in diverse real-world datasets.
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Bandit Algorithms Research · Mobile Crowdsensing and Crowdsourcing
MethodsSoftmax · Attention Is All You Need
