HierTCN: Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems (WWW-2019)
Existing dynamic recommender systems often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequential multi-session interactions with items. We conduct extensive experiments on a public XING dataset and a large-scale Pinterest dataset of 6 million users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than RNN-based models and uses 90% less data memory compared to TCN-based models.