Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representation Learning for cold-start recommendation   A standard approach to Collaborative Filtering (CF), i.e. prediction of user
ratings on items, relies on Matrix Factorization techniques. Representations
for both users and items are computed from the observed ratings and used for
prediction. Unfortunatly, these transductive approaches cannot handle the case
of new users arriving in the system, with no known rating, a problem known as
user cold-start. A common approach in this context is to ask these incoming
users for a few initialization ratings. This paper presents a model to tackle
this twofold problem of (i) finding good questions to ask, (ii) building
efficient representations from this small amount of information. The model can
also be used in a more standard (warm) context. Our approach is evaluated on
the classical CF problem and on the cold-start problem on four different
datasets showing its ability to improve baseline performance in both cases.

15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ