Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Alleviating the recommendation bias via rank aggregation   The primary goal of a recommender system is often known as "helping users
find relevant items", and a lot of recommendation algorithms are proposed
accordingly. However, these accuracy-oriented methods usually suffer the
problem of recommendation bias on popular items, which is not welcome to not
only users but also item providers. To alleviate the recommendation bias
problem, we propose a generic rank aggregation framework for the recommendation
results of an existing algorithm, in which the user- and item-oriented ranking
results are linearly aggregated together, with a parameter controlling the
weight of the latter ranking process. Experiment results of a typical algorithm
on two real-world data sets show that, this framework is effective to improve
the recommendation fairness of any existing accuracy-oriented algorithms, while
avoiding significant accuracy loss.

22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys