Corpus ID: 85459167

Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction

@article{Zhao2019DeepHR,
  title={Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction},
  author={Dongyan Zhao and Liang Zhang and Bo Zhang and Lizhou Zheng and Yongjun Bao and Weipeng P. Yan},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.09374}
}
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… Expand
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References

SHOWING 1-10 OF 34 REFERENCES
Deep Reinforcement Learning for List-wise Recommendations
TLDR
This paper proposes a novel recommender system with the capability of continuously improving its strategies during the interactions with users and introduces an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Expand
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
TLDR
This paper 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. Expand
Deep reinforcement learning for page-wise recommendations
TLDR
A principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page is proposed and a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users is proposed. Expand
Deep Learning Based Recommender System
TLDR
A taxonomy of deep learning-based recommendation models is provided and a comprehensive summary of the state of the art is provided, along with new perspectives pertaining to this new and exciting development of the field. Expand
A hybrid web recommender system based on Q-learning
TLDR
A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior, and it is demonstrated how this method can improve the overall quality of web recommendations. Expand
Data-Efficient Hierarchical Reinforcement Learning
TLDR
This paper studies how to develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. Expand
Deep Reinforcement Learning in Large Discrete Action Spaces
TLDR
This paper leverages prior information about the actions to embed them in a continuous space upon which it can generalize, and uses approximate nearest-neighbor methods to allow reinforcement learning methods to be applied to large-scale learning problems previously intractable with current methods. Expand
FeUdal Networks for Hierarchical Reinforcement Learning
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, andExpand
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Expand
Wide & Deep Learning for Recommender Systems
TLDR
Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow. Expand
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