Name
Abstract | ||
|---|---|---|
Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of a linear Ranking SVM model can be written as a linear combination of the preference pairs, i.e., $\boldsymbol {w} = \sum _{(i,j)} \alpha _{ij} (\boldsymbol {x}_i - \boldsymbol {x}_j)$w=∑(i,j)αij(xi-xj), where $\alpha _{ij}$αij denotes the Lagrange parameters associated with each preference pair $(i,j)$(i,j). It is observed that there exist obvious interactions among the document pairs because two preference pairs could share a same document as their items, e.g., preference pairs $(d_1, d_2)$(d1,d2) and $(d_1, d_3)$(d1,d3) share the document $d_1$d1. Thus it is natural to ask if there also exist interactions over the model parameters $\alpha _{ij}$αij, which may be leveraged to construct better ranking models. This paper aims to answer the question. We empirically found that there exists a low-rank structure over the rearranged Ranking SVM model parameters $\alpha _{ij}$αij, which indicates that the interactions do exist. Based on the discovery, we made modifications on the original Ranking SVM model by explicitly applying low-rank constraints to the Lagrange parameters, achieving two novel algorithms called Factorized Ranking SVM and Regularized Ranking SVM, respectively. Specifically, in Factorized Ranking SVM each parameter $\alpha _{ij}$αij is decomposed as a product of two low-dimensional vectors, i.e., $\alpha _{ij} = \langle \boldsymbol {v}_i, \boldsymbol {v}_j\rangle$αij=〈vi,vj〉, where vectors $\boldsymbol {v}_i$vi and $\boldsymbol {v}_j$vj correspond to document $i$i and $j$j, respectively; In Regularized Ranking SVM, a nuclear norm is applied to the rearranged parameters matrix for controlling its rank. Experimental results on three LETOR datasets show that both of the proposed methods can outperform state-of-the-art learning to rank models including the conventional Ranking SVM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tkde.2018.2851257 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Support vector machines,Training,Matrix decomposition,Analytical models,Linear programming,Matrix converters,Task analysis | Learning to rank,Linear combination,Data mining,Combinatorics,Ranking,Ranking SVM,Matrix (mathematics),Computer science,Matrix norm,Low-rank approximation,Binary number | Journal |
Volume | Issue | ISSN |
31 | 6 | 1041-4347 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Xu | 1 | 1435 | 74.49 |
| Wei Zeng | 2 | 118 | 24.27 |
| Yanyan Lan | 3 | 1005 | 63.59 |
| Jiafeng Guo | 4 | 1737 | 102.17 |
| Xueqi Cheng | 5 | 3148 | 247.04 |