Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: When Collaborative Filtering Meets Reinforcement Learning   In this paper, we study a multi-step interactive recommendation problem,
where the item recommended at current step may affect the quality of future
recommendations. To address the problem, we develop a novel and effective
approach, named CFRL, which seamlessly integrates the ideas of both
collaborative filtering (CF) and reinforcement learning (RL). More
specifically, we first model the recommender-user interactive recommendation
problem as an agent-environment RL task, which is mathematically described by a
Markov decision process (MDP). Further, to achieve collaborative
recommendations for the entire user community, we propose a novel CF-based MDP
by encoding the states of all users into a shared latent vector space. Finally,
we propose an effective Q-network learning method to learn the agent's optimal
policy based on the CF-based MDP. The capability of CFRL is demonstrated by
comparing its performance against a variety of existing methods on real-world
datasets.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con