Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Domain Generalization Perspective on Listwise Context Modeling   As one of the most popular techniques for solving the ranking problem in
information retrieval, Learning-to-rank (LETOR) has received a lot of attention
both in academia and industry due to its importance in a wide variety of data
mining applications. However, most of existing LETOR approaches choose to learn
a single global ranking function to handle all queries, and ignore the
substantial differences that exist between queries. In this paper, we propose a
domain generalization strategy to tackle this problem. We propose
Query-Invariant Listwise Context Modeling (QILCM), a novel neural architecture
which eliminates the detrimental influence of inter-query variability by
learning \textit{query-invariant} latent representations, such that the ranking
system could generalize better to unseen queries. We evaluate our techniques on
benchmark datasets, demonstrating that QILCM outperforms previous
state-of-the-art approaches by a substantial margin.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th