Cross-domain recommendation for large-scale data
Shaghayegh Sahebi, Peter L. Brusilovsky, Vladimir Bobrokov · D-Scholarship@Pitt (University of Pittsburgh) · 2017
Cross-domain algorithms have been introduced to help improving recommendations and to alleviate cold-start problem, especially in small and sparse datasets. These algorithms work by transferring information from source domain(s) to target domain. In this paper, we study if such algorithms can be helpful for large-scale datasets. We introduce a large-scale cross-domain recommender algorithm derived from canonical correlation analysis and analyze its performance, in comparison with single and cross-domain baseline algorithms. Our experiments in both cold-start and hot-start situations show the effectiveness of the proposed approach.