Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Customer Price Preference and Product Profit Role in
  Recommender Systems   Most of the research in the recommender systems domain is focused on the
optimization of the metrics based on historical data such as Mean Average
Precision (MAP) or Recall. However, there is a gap between the research and
industry since the leading Key Performance Indicators (KPIs) for businesses are
revenue and profit. In this paper, we explore the impact of manipulating the
profit awareness of a recommender system. An average e-commerce business does
not usually use a complicated recommender algorithm. We propose an adjustment
of a predicted ranking for score-based recommender systems and explore the
effect of the profit and customers' price preferences on two industry datasets
from the fashion domain. In the experiments, we show the ability to improve
both the precision and the generated recommendations' profit. Such an outcome
represents a win-win situation when e-commerce increases the profit and
customers get more valuable recommendations.

8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc