Abstract: 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%.

Key words: session-based recommendation systems, recurrent neural networks, personalized recommendations