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Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical ...
The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action ...
Conclusion In this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework to conduct large-scale interactive recommendation. ...
doi:10.1609/aaai.v33i01.33013312
fatcat:tc7eeizjvnbslk3yqguegkg3ga
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
[article]
2018
arXiv
pre-print
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To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical ...
The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action ...
Conclusion In this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework to conduct large-scale interactive recommendation. ...
arXiv:1811.05869v1
fatcat:6ulcdo4py5crbaksrbubxghmqm
A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation
[article]
2020
arXiv
pre-print
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To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. ...
IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. ...
[4] proposed a tree-structured policy gradient recommendation (TPGR) framework, within which a balanced hierarchical clustering tree is built over the items. ...
arXiv:2004.06651v4
fatcat:57f2nh7aj5hqdpcxcyncvhlgee
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
[article]
2022
arXiv
pre-print
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Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. ...
CopyAttack is constructed to both efficiently and effectively learn policy gradient networks that first select, and then further refine/craft, user profiles from the source domain to ultimately copy into ...
Here, the main challenges are how to handle a large-scale discrete action space (i.e., set of all user profiles) as well as achieve satisfied results under limited resources to interact with the target ...
arXiv:2005.08147v2
fatcat:s25ta4ulrza3zcb6na56nagony
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
[article]
2020
arXiv
pre-print
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Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. ...
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. ...
Tree-structured Policy Gradient Recommendation (TPGR) [25] : A state-of-the-art model that uses reinforcement learning and binary tree for large-scale interactive recommendation. ...
arXiv:2004.08068v1
fatcat:ijcquqegl5bg7jhersiotifene
A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation
2021
ACM/IMS Transactions on Data Science
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In the TRGIR implementation with Deep Deterministic Policy Gradient (DDPG), denoted as TRGIR-DDPG, we design a policy vector, which can represent user's preferences, to generate discrete actions from the ...
Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in Interactive Recommender Systems (IRSs). ...
Most recently, based on Deterministic Policy Gradient (DPG), Chen et al. [5] proposed a Tree-structured Policy Gradient Recommendation (TPGR) framework. ...
doi:10.1145/3522596
fatcat:k5fajvd3frey7fxzasz3h2pwdi
Nurturing resilient forest biodiversity: nest webs as complex adaptive systems
2020
Ecology and Society
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We use the idea of panarchy (interacting adaptive cycles at multiple spatio-temporal scales) to expand the nest web concept to levels from single tree to biome. ...
Forests are complex adaptive systems in which properties at higher levels emerge from localized networks of many entities interacting at lower levels, allowing the development of multiple ecological pathways ...
Although cavities are hosted by individual trees, the memory processes of these individuals are derived from interaction networks with other organisms at larger scales. ...
doi:10.5751/es-11590-250227
fatcat:o2ceayn3kzc4tnz7khnoe7rxom
Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi‐scale approach
2020
Ecology and Evolution
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The study design is based on a pool of 135 plots (1 ha) distributed along gradients of forest connectivity and structure. ...
social and economic studies of biodiversity conservation across multiple spatial scales. ...
B3 Diversity and functions of plant-insect interactions along a retention gradient The project addresses the overall hypothesis that stand-scale retention measures influence the diversity and trophic interactions ...
doi:10.1002/ece3.6003
pmid:32076529
pmcid:PMC7029101
fatcat:p3jp4y6odzcz7igx2kdrd2vqcu
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
[article]
2021
arXiv
pre-print
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We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. ...
of the recent trends of deep reinforcement learning in recommender systems. ...
[11] propose TPGR, which designs a tree-structured policy gradient method to handle the large discrete action space hierarchically. ...
arXiv:2109.03540v2
fatcat:5gwrbfcj3rc7jfkd54eseck5ga
Towards Sample Efficient Reinforcement Learning
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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We also discuss some challenges in real-world applications, with the hope of inspiring future researches. ...
With deep models, reinforcement learning has shown great potential in complex tasks such as playing games from pixels. ...
Very large action space. A large-scale recommendation system usually have a large number of items, say tens of thousands, to recommend. ...
doi:10.24963/ijcai.2018/820
dblp:conf/ijcai/Yu18
fatcat:rhoz76vu2jfr3gc2zufhzhtppq
Online Machine Learning in Big Data Streams
[article]
2018
arXiv
pre-print
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Compared to past surveys, our article is different because we discuss recommender systems in extended detail. ...
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. ...
Parameterized policies are typically optimized by gradient ascent with respect to the performance of the policy. ...
arXiv:1802.05872v1
fatcat:uhm3vscaebbfho3w2apfq46zse
What Are Intermediate-Severity Forest Disturbances and Why Are They Important?
2018
Forests
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Based on these characteristics, disturbances are placed into one of three broad categories, gap-scale, intermediate-severity, or catastrophic disturbance, along the disturbance classification gradient. ...
based on the influence of residual trees on the composition of the regeneration layer. ...
Gap-scale disturbances are caused by the removal of a single canopy tree, a small cluster of trees, or even a large branch from a canopy dominant individual. ...
doi:10.3390/f9090579
fatcat:mvlftyocrvdhjofjg24wspy3fu
A Survey on Reinforcement Learning for Recommender Systems
[article]
2022
arXiv
pre-print
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To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational ...
Empirical results show that RL-based recommendation methods often surpass most of supervised learning methods, owing to the interactive nature and autonomous learning ability. ...
To solve this problem, [60] proposes a Tree-structured Policy Gradient Recommendation framework (TPGR) to achieve high effectiveness and efficiency for largescale interactive recommendations. ...
arXiv:2109.10665v2
fatcat:wx5ghn66hzg7faxee54jf7gspq
Interactions Of Forests, Climate, Water Resources, And Humans In A Changing Environment: Research Needs
2013
Zenodo
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Studies in this collection of six papers cover a wide range of geographic regions from Australia to Nigeria with spatial research scale spanning from a tree leaf, to a segment of forest road, and large ...
Future research should focus on feedbacks among forests, climate, water, and disturbances, and interactions of ecohydrologic systems, economics and policies using an integrated approach. ...
A recent study in China suggests that there remains large uncertainty about the effects of policy-driven large scale ecological restoration on the sustainability of ecological rehabilitation performance ...
doi:10.5281/zenodo.8100
fatcat:btkg57i6dnf7dgkybdzk2woahi
Interactions of Forests, Climate, Water Resources, and Humans in a Changing Environment: Research Needs
2013
British Journal of Environment and Climate Change
Preserved Fulltext
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The file type is application/pdf.
Studies in this collection of six papers cover a wide range of geographic regions from Australia to Nigeria with spatial research scale spanning from a tree leaf, to a segment of forest road, and large ...
Future research should focus on feedbacks among forests, climate, water, and disturbances, and interactions of ecohydrologic systems, economics and policies using an integrated approach. ...
A recent study in China suggests that there remains large uncertainty about the effects of policy-driven large scale ecological restoration on the sustainability of ecological rehabilitation performance ...
doi:10.9734/bjecc/2013/6212
fatcat:b36onst66fcxzmd2z4bullvvba
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