A Generic Learning Framework for Sequential Recommendation with Distribution Shifts

Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, Xiang Wang

Abstract:

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization (ERM) as the learning framework, which
inherently assumes that the training data (historical interaction
sequences) and the testing data (future interactions) are drawn
from the same distribution. However, such i.i.d. assumption hardly
holds in practice, due to the online serving and dynamic nature of
recommender system. For example, with the streaming of new data,
the item popularity distribution would change, and the user preference would evolve after consuming some items. Such distribution
shifts could undermine the ERM framework, hurting the model’s
generalization ability for future online serving.
In this work, we aim to develop a generic learning framework to
enhance the generalization of recommenders in the dynamic environment. Specifically, on top of ERM, we devise a Distributionally
Robust Optimization mechanism for SeqRec (DROS). At its core
is our carefully-designed distribution adaption paradigm, which
considers the dynamics of data distribution and explores possible
distribution shifts between training and testing. Through this way,
we can endow the backbone recommenders with better generalization ability. It is worth mentioning that DROS is an effective
model-agnostic learning framework, which is applicable to general
recommendation scenarios. Theoretical analyses show that DROS
enables the backbone recommenders to achieve robust performance
in future testing data. Empirical studies verify the effectiveness
against dynamic distribution shifts of DROS. Codes are anonymously open-sourced at https://github.com/YangZhengyi98/DROS.