1,884 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Deep Offline Reinforcement Learning for Real-World Treatment Optimization Applications
There is increasing interest in data-driven approaches for dynamically
choosing optimal treatment strategies in many chronic disease management and
critical care applications. Reinforcement learning methods are well-suited to
this sequential decision-making problem, but must be trained and evaluated
exclusively on retrospective medical record datasets as direct online
exploration is unsafe and infeasible. Despite this requirement, the vast
majority of dynamic treatment optimization studies use off-policy RL methods
(e.g., Double Deep Q Networks (DDQN) or its variants) that are known to perform
poorly in purely offline settings. Recent advances in offline RL, such as
Conservative Q-Learning (CQL), offer a suitable alternative. But there remain
challenges in adapting these approaches to real-world applications where
suboptimal examples dominate the retrospective dataset and strict safety
constraints need to be satisfied. In this work, we introduce a practical
transition sampling approach to address action imbalance during offline RL
training, and an intuitive heuristic to enforce hard constraints during policy
execution. We provide theoretical analyses to show that our proposed approach
would improve over CQL. We perform extensive experiments on two real-world
tasks for diabetes and sepsis treatment optimization to compare performance of
the proposed approach against prominent off-policy and offline RL baselines
(DDQN and CQL). Across a range of principled and clinically relevant metrics,
we show that our proposed approach enables substantial improvements in expected
health outcomes and in consistency with relevant practice and safety
guidelines
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
A fundamental question in any peer-to-peer ride-sharing system is how to,
both effectively and efficiently, meet the request of passengers to balance the
supply and demand in real time. On the passenger side, traditional approaches
focus on pricing strategies by increasing the probability of users' call to
adjust the distribution of demand. However, previous methods do not take into
account the impact of changes in strategy on future supply and demand changes,
which means drivers are repositioned to different destinations due to
passengers' calls, which will affect the driver's income for a period of time
in the future. Motivated by this observation, we make an attempt to optimize
the distribution of demand to handle this problem by learning the long-term
spatio-temporal values as a guideline for pricing strategy. In this study, we
propose an offline deep reinforcement learning based method focusing on the
demand side to improve the utilization of transportation resources and customer
satisfaction. We adopt a spatio-temporal learning method to learn the value of
different time and location, then incentivize the ride requests of passengers
to adjust the distribution of demand to balance the supply and demand in the
system. In particular, we model the problem as a Markov Decision Process (MDP)
Advances in Reinforcement Learning
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic
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