Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain
recommendation (CDR) has been proposed to leverage the relatively richer …

… cross-domain collaboration data from research publications and confirm the above patterns.
We propose the Cross-domain … dates the existing cross-domain collaborations through topic …

… To avoid costly pairing, we address the task of discovering cross-domain relations when
given unpaired data. We propose a method based on generative adversarial networks that …

We study the problem of transferring a sample in one domain to an analog sample in another
domain. Given two related domains, S and T, we would like to learn a generative function …

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have
been proposed to improve the recommendation accuracy in a target dataset (domain/system) …

… cross-domain … of cross-domain recommendation that classifies different recommendation
scenarios and recommendation tasks. We then introduce and summarize existing cross-domain …

… Multi-level Patch-based Loss To solve the aforementioned problems and to improve the
cross-domain detection, we adopt the idea of layer-wise adaptation [10, 15]. We extract the …

… We enforce this constraint using a cross-domain consistency loss that provides additional …
Second, we propose a cross-domain consistency loss that provides additional supervisory …

… cross-domain LM as a bridge cross-domains for NER domain adaptation, performing
crossdomain … can effectively extract domain differences from crossdomain LM contrast, allowing …