在线推荐系统中的快速分布式Bandits算法
Fast Distributed Bandits for Online Recommendation Systems
摘要：
上下文强盗算法通常用于推荐系统，其中内容流行度可以快速变化。这些算法根据与用户和项目相关联的上下文，不断学习用户和项目之间的潜在映射。最近的推荐算法，学习聚类或用户之间的社会结构，表现出更高的推荐精度。然而，随着环境中的用户和项目数量的增加，生成推荐所需的时间显著恶化。因此，实际上无法部署这些系统。最先进的分布式强盗算法- DCCB -依赖于对等网络来在分布式工作者之间共享信息。然而，这种方法不能很好地随着用户数量的增加而扩展。此外，它遭受缓慢的集群发现，导致准确性下降。针对上述问题，提出了一种基于带宽的分布式算法DistCLUB。该算法以分布式的方式惰性地创建集群，极大地降低了网络数据共享需求，实现了很高的可扩展性。此外，DistCLUB查找聚类的速度要快得多，比最先进的算法实现了更好的准确性。在真实世界的基准测试和合成数据集上的评估表明，DistCLUB平均比DCCB快8.87倍，并且实现了14.5%的归一化预测性能。
英文摘要：
Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between users and items, based on contexts associated with them both. Recent recommendation algorithms that learn clustering or social structures between users have exhibited higher recommendation accuracy. However, as the number of users and items in the environment increases, the time required to generate recommendations deteriorates significantly. As a result, these cannot be deployed in practice. The state-of-the-art distributed bandit algorithm - DCCB - relies on a peer-to-peer net-work to share information among distributed workers. However, this approach does not scale well with the increasing number of users. Furthermore, it suffers from slow discovery of clusters, resulting in accuracy degradation. To address the above issues, this paper proposes a novel distributed bandit-based algorithm called DistCLUB. This algorithm lazily creates clusters in a distributed manner, and dramatically reduces the network data sharing requirement, achieving high scalability. Additionally, DistCLUB finds clusters much faster, achieving better accuracy than the state-of-the-art algorithm. Evaluation over both real-world benchmarks and synthetic datasets shows that DistCLUB is on average 8.87x faster than DCCB, and achieves 14.5% higher normalized prediction performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
