论文标题
基于群集的深层上下文强化学习top-k建议
Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations
论文作者
论文摘要
在过去的几十年中，电子商务领域的快速发展导致了即将需要个性化，高效和动态的推荐系统。为了充分满足这一需求，我们提出了一种新颖的方法来通过创建与增强学习的聚类合奏来生成TOP-K建议。我们已经合并了DB扫描聚类，以解决庞大的物品空间，因此效率多倍。此外，通过使用深层的上下文加强学习，我们提出的工作利用用户的功能充分发挥了潜力。通过部分更新和批处理更新，该模型将不断学习用户模式。与最先进的策略相比，基于决斗的匪徒的探索提供了强大的探索。在公共数据集上进行的详细实验验证了我们对我们技术效率的主张，该效率与现有技术相吻合。
Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The Duelling Bandit based exploration provides robust exploration as compared to the state-of-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as com-pared to existing techniques.
