Jul 7, 2022 · We introduce a fresh perspective to solve cross-domain recommendation (CDR) by learning disentangled domain-shared/domain-specific ...
The source code is for the paper: “DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation” accepted in SIGIR 2022 by Jiangxia Cao, ...
ABSTRACT. Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-Domain Recommendation (CDR) has attracted.
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It manually designed heuristic rules using degree information to facilitate knowledge transfer. • DisenCDR [18] is a variational bipartite graph-based cross- ...
Oct 31, 2019 · In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user ...
Aug 24, 2024 · Modern recommendation platforms frequently encompass multiple domains to cater to the varied preferences of users. Recently, cross-domain ...
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What is disentangled representation learning?
What is cross domain learning?
Disentangled representation learning and contrastive learning are widely used in cross-domain recommendation. Disentangled representation learning can ...
In CD2CDR, a causal deconfounding framework via confounder disentanglement is proposed to preserve the positive effects of observed confounders and ...
Jul 1, 2024 · CDR (Cross-Domain Recommendation), i.e., leveraging information from multiple domains, is a critical solution to data sparsity problem in ...