Recommender systems that can learn from cross-session data to dynamically
predict the next item a user will choose are crucial for online platforms.
However, existing approaches 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, which
is crucial for effective recommendations. 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. HierTCN is designed for web-scale systems with
billions of items and hundreds of millions of users. It consists of two levels
of models: The high-level model uses Recurrent Neural Networks (RNN) to
aggregate users' evolving long-term interests across different sessions, while
the low-level model is implemented with Temporal Convolutional Networks (TCN),
utilizing both the long-term interests and the short-term interactions within
sessions to predict the next interaction. We conduct extensive experiments on a
public XING dataset and a large-scale Pinterest dataset that contains 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. We
further develop an effective data caching scheme and a queue-based mini-batch
generator, enabling our model to be trained within 24 hours on a single GPU.
Our model consistently outperforms state-of-the-art dynamic recommendation
methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.

本研究提出了一种使用 Hierarchical Temporal Convolutional Networks (HierTCN) 的推荐系统，以动态预测用户将选择的下一个物品，该模型由两个级别的模型组成，可用于拥有大量用户和物品的 Web 规模系统，并可以超越动态推荐方法的最新水平。