Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PMD: An Optimal Transportation-based User Distance for Recommender
  Systems   Collaborative filtering, a widely-used recommendation technique, predicts a
user's preference by aggregating the ratings from similar users. As a result,
these measures cannot fully utilize the rating information and are not suitable
for real world sparse data. To solve these issues, we propose a novel user
distance measure named Preference Mover's Distance (PMD) which makes full use
of all ratings made by each user. Our proposed PMD can properly measure the
distance between a pair of users even if they have no co-rated items. We show
that this measure can be cast as an instance of the Earth Mover's Distance, a
well-studied transportation problem for which several highly efficient solvers
have been developed. Experimental results show that PMD can help achieve
superior recommendation accuracy than state-of-the-art methods, especially when
training data is very sparse.

23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph