Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Embarrassingly Shallow Autoencoders for Sparse Data   Combining simple elements from the literature, we define a linear model that
is geared toward sparse data, in particular implicit feedback data for
recommender systems. We show that its training objective has a closed-form
solution, and discuss the resulting conceptual insights. Surprisingly, this
simple model achieves better ranking accuracy than various state-of-the-art
collaborative-filtering approaches, including deep non-linear models, on most
of the publicly available data-sets used in our experiments.

31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol