面向会话型推荐系统的个性化分层循环模型

Personalized Hierarchical Recurrent Model for Session-Based Recommendation Systems

为了精准地捕捉用户行为模式,引入中期兴趣的概念,提出一个基于循环神经网络(RNN)的个性化分层循环模型,通过在同一框架下联合利用用户的会话、区块和全部行为序列来学习用户的综合兴趣.利用一个捕捉会话内序列模式的会话级RNN建模用户的短期兴趣;设计了一个捕捉区块内相邻会话关联关系的区块级RNN,进一步描述用户的中期兴趣;使用一个用户级RNN追踪长期兴趣的演化;引入带有不同交互机制的融合层,以有效融合不同层次的兴趣信息.在3个真实数据集上进行实验,结果表明,该方法与先进的推荐方法相比,Recall@10提升了18.35%.

The existing studies of session-based recommendations mainly focus on the short-term and long-term interests of users.In order to accurately depict behavior patterns of users,the author introduces the medium-term interests and proposes personalized hierarchical recurrent model(PHRM)based on recurrent neural networks(RNNs),to learn a comprehensive description of user interests by jointly leveraging session,block and global behaviors in a unified framework.First,to model short-term interests,a session-level RNN is designed to capture sequential patterns in sessions.Next,to further describe medium-term interests,a block-level RNN is added to capture correlations across sessions in a block.Then,a user-level RNN is devised to track evolution of long-term interests.Finally,the article designs fusion layers with different interaction mechanisms to effectively integrate cross-level interest information.Simulations on three real-world datasets show that PHRM outperforms the state-of-the-art recommendation methods,with Recall@10 increasing by 18.35%.