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 and transferable oncepts. We solve this problem by optimizing the convex upper bound of the non-continuous loss function and derive its generalization bound. Experimental results on three different genres of data sets demonstrate the effectiveness of the proposed approach.
heterogeneous cross domain ranking learning to rank transfer ranking