  Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper, we propose a novel recommender system with the
capability of continuously improving its strategies during the interactions
with users. We model the sequential interactions between users and a
recommender system as a Markov Decision Process (MDP) and leverage
Reinforcement Learning (RL) to automatically learn the optimal strategies via
recommending trial-and-error items and receiving reinforcements of these items
from users' feedback. Users' feedback can be positive and negative and both
types of feedback have great potentials to boost recommendations. However, the
number of negative feedback is much larger than that of positive one; thus
incorporating them simultaneously is challenging since positive feedback could
be buried by negative one. In this paper, we develop a novel approach to
incorporate them into the proposed deep recommender system (DEERS) framework.
The experimental results based on real-world e-commerce data demonstrate the
effectiveness of the proposed framework. Further experiments have been
conducted to understand the importance of both positive and negative feedback
in recommendations.
