Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Energy-Based Sequence GANs for Recommendation and Their Connection to
  Imitation Learning   Recommender systems aim to find an accurate and efficient mapping from
historic data of user-preferred items to a new item that is to be liked by a
user. Towards this goal, energy-based sequence generative adversarial nets
(EB-SeqGANs) are adopted for recommendation by learning a generative model for
the time series of user-preferred items. By recasting the energy function as
the feature function, the proposed EB-SeqGANs is interpreted as an instance of
maximum-entropy imitation learning.

10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat