Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Field-aware Factorization Machines in a Real-world Online Advertising
  System   Predicting user response is one of the core machine learning tasks in
computational advertising. Field-aware Factorization Machines (FFM) have
recently been established as a state-of-the-art method for that problem and in
particular won two Kaggle challenges. This paper presents some results from
implementing this method in a production system that predicts click-through and
conversion rates for display advertising and shows that this method it is not
only effective to win challenges but is also valuable in a real-world
prediction system. We also discuss some specific challenges and solutions to
reduce the training time, namely the use of an innovative seeding algorithm and
a distributed learning mechanism.

32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con