Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Rank Aggregation for Expert Search in Academic Digital Libraries   The task of expert finding has been getting increasing attention in
information retrieval literature. However, the current state-of-the-art is
still lacking in principled approaches for combining different sources of
evidence. This paper explores the usage of unsupervised rank aggregation
methods as a principled approach for combining multiple estimators of
expertise, derived from the textual contents, from the graph-structure of the
citation patterns for the community of experts, and from profile information
about the experts. We specifically experimented two unsupervised rank
aggregation approaches well known in the information retrieval literature,
namely CombSUM and CombMNZ. Experiments made over a dataset of academic
publications for the area of Computer Science attest for the adequacy of these
methods.

26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
16
The categories of this paper are: stat
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat