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.
[1]
Qiang Yang,et al.
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
,
2009,
IJCAI.
[2]
Yi-Hsuan Yang,et al.
Query-based Music Recommendations via Preference Embedding
,
2016,
RecSys.
[3]
Steven Skiena,et al.
DeepWalk: online learning of social representations
,
2014,
KDD.
[4]
Paolo Cremonesi,et al.
Cross-Domain Recommender Systems
,
2011,
2011 IEEE 11th International Conference on Data Mining Workshops.
[5]
Qiaozhu Mei,et al.
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
,
2015,
KDD.