Research Area:  Machine Learning

Abstract:

An effective online recommendation system should jointly capture users long-term and short-term preferences in both users internal behaviors (from the target recommendation task) and external behaviors (from other tasks). However, it is extremely challenging to conduct fast adaptations to real-time new trends while making full use of all historical behaviors in large-scale systems, due to the real-world limitations in real-time training efficiency and external behavior acquisition. To address these practical challenges, we propose a novel Long Short-Term Temporal Meta-learning framework (LSTTM) for online recommendation. It arranges user multi-source behaviors in a global long-term graph and an internal short-term graph, and conducts different GAT-based aggregators and training strategies to learn user short-term and long-term preferences separately. To timely capture users real-time interests, we propose a temporal meta-learning method based on MAML under an asynchronous optimization strategy for fast adaptation, which regards recommendations at different time periods as different tasks. In experiments, LSTTM achieves significant improvements on both offline and online evaluations. It has been deployed on a widely-used online recommendation system named WeChat Top Stories, affecting millions of users.

Keywords:  
Long Short-Term Temporal Meta-learning framework
Meta-learning
GAT-based aggregators
Recommendation system

Author(s) Name:  Ruobing Xie , Yalong Wang , Rui Wang , Yuanfu Lu , Yuanhang Zou

Publisher name:  ACM Library

DOI:  10.1145/3488560.3498371

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3488560.3498371