Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645953.1646079 | International Conference on Information and Knowledge Management, Proceedings, |
Keywords | Field | DocType |
training data,different genre,heterogenous domain,heterogeneous domain,heterogeneous cross domain ranking,large number,large amount,different information source,data source,latent space,learning to rank,loss function,upper bound | Data source,Training set,Learning to rank,Data mining,Data set,Ranking,Computer science,Upper and lower bounds,Bridging (networking),Regular polygon | Conference |
Citations | PageRank | References |
18 | 0.60 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Wang | 1 | 109 | 4.76 |
| Jie Tang | 2 | 5871 | 300.22 |
| Wei Fan | 3 | 4205 | 253.58 |
| Songcan Chen | 4 | 4148 | 191.89 |
| Zi Yang | 5 | 33 | 5.48 |
| Yan-zhu Liu | 6 | 22 | 2.18 |