Research on interactive recommendation system based on reinforcement learning

2021 ◽  
Author(s):  
Qifan Wang ◽  
Hongwei Shi
Keyword(s):  
Reinforcement Learning ◽  
Recommendation System ◽  
Interactive Recommendation

scholarly journals Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method for Electricity Optimization in Smart Homes

2021 ◽  
Author(s):  
Salman Sadiq Shuvo ◽  
Yasin Yilmaz
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Recommendation System ◽  
Smart Homes ◽  
Electricity Price ◽  
Learning Method

Aras activity dataset<div>NYISO dynamic electricity price</div><div>A2C implementation in Python</div><div><br></div><div>Article under review in IEEE Transactions on Smart Grid </div>

Recommendation System using Reinforcement Learning for What-If Simulation in Digital Twin

2021 ◽  
Author(s):  
Flavia Pires ◽  
Bilal Ahmad ◽  
Antonio Paulo Moreira ◽  
Paulo Leitao
Keyword(s):  
Reinforcement Learning ◽  
Recommendation System ◽  
Digital Twin

scholarly journals Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 1-21
Author(s):  
Pengzhan Guo ◽  
Keli Xiao ◽  
Zeyang Ye ◽  
Wei Zhu
Keyword(s):  
Reinforcement Learning ◽  
Spatial Data ◽  
Urban Areas ◽  
Recommendation System ◽  
Service Providers ◽  
Route Optimization ◽  
Spatial Data Analysis ◽  
Parameter Determination ◽  
Sequential Decision ◽  
Taxi Drivers

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).

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