CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation

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 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.