1 research outputs found
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
The recommender system is an important form of intelligent application, which
assists users to alleviate from information redundancy. Among the metrics used
to evaluate a recommender system, the metric of conversion has become more and
more important. The majority of existing recommender systems perform poorly on
the metric of conversion due to its extremely sparse feedback signal. To tackle
this challenge, we propose a deep hierarchical reinforcement learning based
recommendation framework, which consists of two components, i.e., high-level
agent and low-level agent. The high-level agent catches long-term sparse
conversion signals, and automatically sets abstract goals for low-level agent,
while the low-level agent follows the abstract goals and interacts with
real-time environment. To solve the inherent problem in hierarchical
reinforcement learning, we propose a novel deep hierarchical reinforcement
learning algorithm via multi-goals abstraction (HRL-MG). Our proposed algorithm
contains three characteristics: 1) the high-level agent generates multiple
goals to guide the low-level agent in different stages, which reduces the
difficulty of approaching high-level goals; 2) different goals share the same
state encoder parameters, which increases the update frequency of the
high-level agent and thus accelerates the convergence of our proposed
algorithm; 3) an appreciate benefit assignment function is designed to allocate
rewards in each goal so as to coordinate different goals in a consistent
direction. We evaluate our proposed algorithm based on a real-world e-commerce
dataset and validate its effectiveness.Comment: submitted to SIGKDD 201