Cross-domain recommendation (CDR) aims to leverage the users' behaviors in
both source and target domains to improve the target domain's performance.
Conventional CDR methods typically explore the dual relations between the
source and target domains' behavior sequences. However, they ignore modeling
the third sequence of mixed behaviors that naturally reflects the user's global
preference. To address this issue, we present a novel and model-agnostic Triple
sequence learning for cross-domain recommendation (Tri-CDR) framework to
jointly model the source, target, and mixed behavior sequences in CDR.
Specifically, Tri-CDR independently models the hidden user representations for
the source, target, and mixed behavior sequences, and proposes a triple
cross-domain attention (TCA) to emphasize the informative knowledge related to
both user's target-domain preference and global interests in three sequences.
To comprehensively learn the triple correlations, we design a novel triple
contrastive learning (TCL) that jointly considers coarse-grained similarities
and fine-grained distinctions among three sequences, ensuring the alignment
while preserving the information diversity in multi-domain. We conduct
extensive experiments and analyses on two real-world datasets with four
domains. The significant improvements of Tri-CDR with different sequential
encoders on all datasets verify the effectiveness and universality. The source
code will be released in the future.Comment: 11 pages, 5 figures, under revie