User Invariant Preference Learning for Multi-Behavior Recommendation

TL;DR

This paper introduces UIPL, a novel method for multi-behavior recommendation that learns users' intrinsic preferences by leveraging invariant risk minimization and variational autoencoders to improve recommendation accuracy.

Contribution

It proposes a user invariant preference learning framework that captures intrinsic user interests across behaviors, reducing noise from auxiliary behaviors using invariant risk minimization.

Findings

UIPL outperforms state-of-the-art methods on four real-world datasets.

The approach effectively captures users' intrinsic preferences.

Experimental results demonstrate improved recommendation accuracy.

Abstract

In multi-behavior recommendation scenarios, analyzing users' diverse behaviors, such as click, purchase, and rating, enables a more comprehensive understanding of their interests, facilitating personalized and accurate recommendations. A fundamental assumption of multi-behavior recommendation methods is the existence of shared user preferences across behaviors, representing users' intrinsic interests. Based on this assumption, existing approaches aim to integrate information from various behaviors to enrich user representations. However, they often overlook the presence of both commonalities and individualities in users' multi-behavior preferences. These individualities reflect distinct aspects of preferences captured by different behaviors, where certain auxiliary behaviors may introduce noise, hindering the prediction of the target behavior. To address this issue, we propose a user invariant preference learning for multi-behavior recommendation (UIPL for short), aiming to capture users' intrinsic interests (referred to as invariant preferences) from multi-behavior interactions to mitigate the introduction of noise. Specifically, UIPL leverages the paradigm of invariant risk minimization to learn invariant preferences. To implement this, we employ a variational autoencoder (VAE) to extract users' invariant preferences, replacing the standard reconstruction loss with an invariant risk minimization constraint. Additionally, we construct distinct environments by combining multi-behavior data to enhance robustness in learning these preferences. Finally, the learned invariant preferences are used to provide recommendations for the target behavior. Extensive experiments on four real-world datasets demonstrate that UIPL significantly outperforms current state-of-the-art methods.