Heterogeneous cross domain ranking in latent space

Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in many real-world applications, in particular with the rapid growth of the Web 2.0, ranking over multiple interrelated (heterogeneous) domains becomes a common situation, where in some domains we may have a large amount of training data while in some other domains we can only collect very little. One important question is: “if there is not sufficient supervision in the domain of interest, how could one borrow labeled information from a related but heterogenous domain to build an accurate model?”. This paper explores such an approach by bridging two heterogeneous domains via the latent space. We propose a regularized framework to simultaneously minimize two loss functions corresponding to two related but different information sources, by mapping each domain onto a “shared latent space”, capturing similar...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang, Yanzhu Liu
• Keywords: CIKM 2009, Heterogeneous Domains, Latent Space, Loss Function