Multimedia recommendation involves personalized ranking tasks, where
multimedia content is usually represented using a generic encoder. However,
these generic representations introduce spurious correlations that fail to
reveal users' true preferences. Existing works attempt to alleviate this
problem by learning invariant representations, but overlook the balance between
independent and identically distributed (IID) and out-of-distribution (OOD)
generalization. In this paper, we propose a framework called Pareto Invariant
Representation Learning (PaInvRL) to mitigate the impact of spurious
correlations from an IID-OOD multi-objective optimization perspective, by
learning invariant representations (intrinsic factors that attract user
attention) and variant representations (other factors) simultaneously.
Specifically, PaInvRL includes three iteratively executed modules: (i)
heterogeneous identification module, which identifies the heterogeneous
environments to reflect distributional shifts for user-item interactions; (ii)
invariant mask generation module, which learns invariant masks based on the
Pareto-optimal solutions that minimize the adaptive weighted Invariant Risk
Minimization (IRM) and Empirical Risk (ERM) losses; (iii) convert module, which
generates both variant representations and item-invariant representations for
training a multi-modal recommendation model that mitigates spurious
correlations and balances the generalization performance within and cross the
environmental distributions. We compare the proposed PaInvRL with
state-of-the-art recommendation models on three public multimedia
recommendation datasets (Movielens, Tiktok, and Kwai), and the experimental
results validate the effectiveness of PaInvRL for both within- and
cross-environmental learning.

基于 Pareto 不变表示学习 (PaInvRL) 框架，同时学习不变表示和变体表示，减轻由通用编码器引入的错误相关性并提供良好的泛化性能。