Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation

Weiming Liu, Chaochao Chen, Jiahe Xu, Xinting Liao, Fan Wang, Xiaolin Zheng, Zhihui Fu, Ruiguang Pei, Jun Wang

Track: Graph algorithms and modeling for the Web

Keywords: Recommendation, Cross-domain recommendation, Multi-modal cross-domain recommendation

TL;DR: We conduct a novel model SIEOUG for solving multi-modal cross-domain recommendation problem

Abstract:

Cross-Domain Recommendation (CDR) has been widely investi-
gated for solving long-standing data sparsity problem via knowl-
edge sharing across domains. In this paper, we focus on the Multi-
Modal Cross-Domain Recommendation (MMCDR) problem where
different items have multi-modal information while few users are
overlapped across domains. MMCDR is particularly challenging
in two aspects: fully exploiting diverse multi-modal information
within each domain and leveraging useful knowledge transfer
across domains. However, previous methods fail to cluster items
with similar characteristics while filtering out inherit noises within
different modalities, hurdling the model performance. What is
worse, conventional CDR models primarily rely on overlapped
users for domain adaptation, making them ill-equipped to handle
scenarios where the majority of users are non-overlapped. To fill
this gap, we propose Joint Similarity Item Exploration and Over-
lapped User Guidance (SIEOUG) for solving the MMCDR problem.
SIEOUG first proposes similarity item exploration module, which
not only obtains pair-wise and group-wise item-item graph knowl-
edge, but also reduces irrelevant noise for multi-modal modeling.
Then SIEOUG proposes user-item collaborative filtering module
to aggregate user/item embeddings with the attention mechanism
for collaborative filtering. Finally SIEOUG proposes overlapped
user guidance module with optimal user matching for knowledge
sharing across domains. Our empirical study on Amazon dataset
with several different tasks demonstrates that SIEOUG significantly
outperforms the state-of-the-art models under the MMCDR setting

Submission Number: 455