Superhighway: Bypass Data Sparsity in Cross-Domain CF
This addresses data sparsity for cross-domain recommendation systems, offering an incremental improvement over traditional methods.
The paper tackles data sparsity in cross-domain collaborative filtering by proposing superhighway construction, an explicit relation-enrichment procedure that bypasses multi-hop paths with direct ones to enhance cross-domain connectivity, resulting in significant performance improvements on real-world music and movie datasets.
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.