Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach

TL;DR

This paper introduces CVPM, a novel cross-domain recommendation method that leverages meta-learning and self-supervised learning to transfer user preferences more accurately, especially when overlapping users are sparse.

Contribution

The paper proposes a hybrid meta-learning and self-supervised framework for fine-grained, personalized cross-domain preference transfer, addressing limitations of previous coarse methods.

Findings

CVPM outperforms existing methods in sparse overlapping user scenarios.

The approach effectively enhances preference representations with non-overlapping user signals.

Experimental results demonstrate significant improvements in recommendation accuracy.

Abstract

Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transformation between two domains. Nevertheless, previous coarse-grained preference representations, non-personalized mapping functions, and excessive reliance on overlapping users limit their performance, especially in scenarios where overlapping users are sparse. To address aforementioned challenges, we propose a novel cross-domain approach, namely CVPM. CVPM formalizes cross-domain interest transfer as a hybrid architecture of parametric meta-learning and self-supervised learning, which not only transfers user preferences at a finer level, but also enables signal enhancement with the knowledge of non-overlapping users. Specifically, with deep insights into user preferences and valence preference theory, we believe that there exists significant difference between users' positive preferences and negative behaviors, and thus employ differentiated encoders to learn their distributions. In particular, we further utilize the pre-trained model and item popularity to sample pseudo-interaction items to ensure the integrity of both distributions. To guarantee the personalization of preference transfer, we treat each user's mapping as two parts, the common transformation and the personalized bias, where the network used to generate the personalized bias is output by a meta-learner. Furthermore, in addition to the supervised loss for overlapping users, we design contrastive tasks for non-overlapping users from both group and individual-levels to avoid model skew and enhance the semantics of representations. Exhaustive data analysis and extensive experimental results demonstrate the effectiveness and advancement of our proposed framework.