  Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in
single-domain CF by leveraging knowledge transferred from related domains. Many
traditional methods focus on enriching compared neighborhood relations in CF
directly to address the sparsity problem. In this paper, we propose
superhighway construction, an alternative explicit relation-enrichment
procedure, to improve recommendations by enhancing cross-domain connectivity.
Specifically, assuming partially overlapped items (users), superhighway
bypasses multi-hop inter-domain paths between cross-domain users (items,
respectively) with direct paths to enrich the cross-domain connectivity. The
experiments conducted on a real-world cross-region music dataset and a
cross-platform movie dataset show that the proposed superhighway construction
significantly improves recommendation performance in both target and source
domains.
