自适应协同组合博弈模型提升推荐精准度，动态识别相似用户
CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation
- 双向博弈架构：用户侧用贝叶斯模型动态找相似用户，物品侧生成多样化推荐
- 在三个真实数据集上平均F1提升2.4%，优于现有最优方法
- 适合个性化推荐场景，尤其对无相似邻居的长尾用户表现更优
聚类强化学习带带来了显著进展，通过利用邻近用户的协同信息来捕捉目标用户的偏好。然而，这类方法常缺乏对相似用户的确切定义，在具有独特偏好的用户缺乏合适邻居时面临挑战。此时，依赖误判邻居的差异偏好会降低推荐质量。为此，本文提出自适应协同组合博弈算法（CoCoB）。CoCoB采用创新的双面博弈架构，将强化学习原则应用于用户和物品两端。用户侧使用改进的贝叶斯模型探索用户相似性，基于相似性概率阈值识别邻居；物品侧将物品视为臂，依据用户侧输出生成多样推荐。当存在有效邻居时，协同利用其偏好；否则仅依赖目标用户自身行为。在线性上下文博弈设置下进行后悔分析，并在三个真实数据集上验证，结果表明CoCoB相较当前最优方法平均提升2.4%的F1分数。
原文摘要 · Abstract (English)
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.
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