%0 Journal Article
%A CHU Yun-fei
%A FENG Chun-yan
%A GUO Cai-li
%A WANG Ya-qing
%A ZHOU Hong-hong
%T Personalized Hierarchical Recurrent Model for Session-Based Recommendation Systems
%D 2019
%R 10.13190/j.jbupt.2019-143
%J Journal of Beijing University of Posts and Telecommunications
%P 142-148
%V 42
%N 6
%X 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%.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-143