Article
Energy & Fuels
Pengxiang Zhao, Zhao Yang Dong, Ke Meng, Weicong Kong, Jiajia Yang
Summary: The deregulation of the retail electricity market has resulted in more electricity plans with competitive rates, giving customers greater flexibility in choosing a provider. This paper proposes a feature engineering hybrid collaborative filtering-based electricity plan recommender system, which shows significant improvement in recommendation accuracy according to real market data.
Article
Computer Science, Information Systems
Fahrettin Horasan, Ahmet Hasim Yurttakal, Selcuk Gunduz
Summary: Collaborative filtering is a technique that considers the common characteristics of users and items in recommendation systems. Matrix decompositions, such as SVD and NMF, are widely used in collaborative filtering. In this study, a technique called T-ULVD was used to improve the accuracy and quality of recommendations. Experimental results showed that T-ULVD achieved better results compared to NMF and performed as well as or even better than SVD. This study may provide guidance for future research on solving the cold-start problem and reducing sparsity in collaborative filtering based recommender systems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Economics
Cristian Villalobos, Matias Negrete-Pincetic, Nicolas Figueroa, Alvaro Lorca, Daniel Olivares
Summary: The extensive growth of variable renewable energy integration presents challenges for power systems and markets, requiring flexibility to adapt to changes. Studies on short-term market pricing schemes show the importance of including flexibility features to incentivize agents to meet systemic flexibility requirements.
Article
Computer Science, Artificial Intelligence
F. Ortega, J. Mayor, D. Lopez-Fernandez, R. Lara-Cabrera
Summary: CF4J 2.0 is a framework designed for research experiments based on collaborative filtering, with features like implemented algorithms, quality measures, parallel execution, and abstract classes for developers to customize. The new version focuses on simple deployment, reproducible science, hyper-parameter optimization, data analysis, and community openness as an open-source project.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Urvish Thakker, Ruhi Patel, Manan Shah
Summary: Collaborative Filtering (CF) is a widely used technology in recommender systems, which effectively utilizes information from applications to find similarities and has been applied in various industries. This paper discusses the algorithm and applications of CF in Movie Recommendation System (MRS), as well as challenges and future developments in the field.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Review
Computer Science, Artificial Intelligence
R. J. Kuo, Shu-Syun Li
Summary: With the rapid development of electronic commerce, the recommender system has emerged to assist users' decision-making processes and enable precision marketing. This study utilized the PSO algorithm to solve the problem of data sparsity, while BERT was applied to extract consumer feedback characteristics. The combination of different data types improved the recommendation performance, as evidenced by outperforming existing methods on Amazon datasets in terms of mean absolute error and mean squared error.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Silvana Vanesa Aciar, Ramon Fabregat, Teodor Jove, Gabriela Aciar
Summary: Recommender systems are essential in addressing information overload, providing opinions and experiences that influence user purchasing decisions. This work presents a product recommender system based on collaborative filtering, filtering reviews and offering necessary answers to assist users.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Hanane Zitouni, Souham Meshoul, Chaker Mezioud
Summary: This paper introduces a double contribution of a two-dimensional context-aware collaborative recommender system (2DCCRS) and a related application. The 2DCCRS framework splits the context into internal and external parts to effectively address the complexity of the context model. It also introduces the concepts of stakeholders and aggregation to tackle the challenges of new users and new items. A case study of the 2DCCRS framework demonstrates its usefulness and effectiveness.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Energy & Fuels
Chenyu Wu, Wei Gu, Enbo Luo, Xi Chen, Hai Lu, Zhongkai Yi
Summary: With the emergence of competitive markets, real-time market clearing results are used to determine optimal power dispatch, which strengthens the coupling between market updates and physical response of generators and networks. Stability analysis for such coupled systems is necessary. Based on the primal-dual gradient method, an economic cybernetic model is established to characterize market operation and power system dynamics. The proposed dynamic model constructs a state space where market participants' actions are control signals and system states are treated as state variables. The transaction process aims to maximize social welfare and frequency regulation at the equilibrium point.
Article
Computer Science, Information Systems
Kalyan Kumar Jena, Sourav Kumar Bhoi, Tushar Kanta Malik, Kshira Sagar Sahoo, N. Z. Jhanjhi, Sajal Bhatia, Fathi Amsaad
Summary: e-Learning is a popular choice for learners in pandemic situations. Recommender systems play an important role in helping users select the best course options based on their preferences. This study proposes a recommender system that uses collaborative filtering mechanism and MI-based models such as KNN, SVD, and NCF for e-Learning course recommendation. Results show that KNN outperforms other models in terms of higher HR and ARHR and lower MAE values.
Article
Energy & Fuels
A. Rezaee Jordehi, V. Sohrabi Tabar, S. A. Mansouri, M. Nasir, S. M. Hakimi, S. Pirouzi
Summary: This research proposes a risk-averse two-stage stochastic model for short-term scheduling of retailers. The results show that the contract with withdrawal penalty and contract with option are only activated in scenarios with high demand. The study also investigates the impact of different procurement resources, tariff types, and price-quota curves on retailer profit, risk, and prices.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Interdisciplinary Applications
Yassine Afoudi, Mohamed Lazaar, Mohammed Al Achhab
Summary: Recommendation systems are tools that provide information based on user preferences and behavior, utilizing methods like Collaborative Filtering, Content Based Approach, and neural network techniques. Research shows that a hybrid recommender framework method improves accuracy and efficiency compared to traditional Collaborative Filtering methods.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Computer Science, Artificial Intelligence
Taushif Anwar, V. Uma, Gautam Srivastava
Summary: Collaborative Filtering (CF) and Singular Value Decomposition (SVD)++ are used to implement a new recommendation system approach in this paper, which aims to find similarity between users and items, predict missing ratings, and recommend top-N user-preferred items. Evaluation of the recommender system's performance shows that the proposed approach achieves a lower error rate, especially with the MovieLens 100K dataset.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Engineering, Electrical & Electronic
Tianguang Lu, Xinyu Chen, Michael B. McElroy, Chris P. Nielsen, Qiuwei Wu, Qian Ai
Summary: This article proposes a reinforcement learning-based decision system to help end users optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. The algorithm improves computational and prediction performance by using an improved state framework and a batch Q-learning algorithm integrated with a Kernel approximator. Results show that the proposed decision model effectively reduces cost and energy consumption dissatisfaction for individual users.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Chemistry, Multidisciplinary
Aaron Ling Chi Yi, Dae-Ki Kang
Summary: This paper explores the issue of generating location recommendations by considering user social influence and local expert knowledge, proposing a new collaborative filtering framework called FANA-CF. It has been validated through experiments using real-world datasets, showing slight outperformance compared to traditional collaborative filtering methods and personalized mean approaches.
APPLIED SCIENCES-BASEL
(2021)
Article
Green & Sustainable Science & Technology
Bin Liu, Ke Meng, Zhao Yang Dong
IET RENEWABLE POWER GENERATION
(2020)
Article
Engineering, Electrical & Electronic
Yaran Li, Ke Meng, Zhao Yang Dong, Wang Zhang
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Liang Yuan, Ke Meng, Jingjie Huang, Zhao Yang Dong
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Yubin Jia, Ke Meng, Changyin Sun, Liang Yuan, Zhao Yang Dong
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Shuai Lu, Wei Gu, Ke Meng, Shuai Yao, Bin Liu, Zhao Yang Dong
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Green & Sustainable Science & Technology
Feng Zhang, Zechun Hu, Ke Meng, Lei Ding, Zhaoyang Dong
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2020)
Article
Engineering, Electrical & Electronic
Ernauli Aprilia, Ke Meng, H. H. Zeineldin, Mohamed Al Hosani, Zhao Yang Dong
ELECTRIC POWER SYSTEMS RESEARCH
(2020)
Article
Thermodynamics
Junjie Zheng, Chun Sing Lai, Haoliang Yuan, Zhao Yang Dong, Ke Meng, Loi Lei Lai
Article
Automation & Control Systems
Yuchen Zhang, Zhao Yang Dong, Weicong Kong, Ke Meng
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Green & Sustainable Science & Technology
Tengjun Zuo, Yuchen Zhang, Ke Meng, Zhao Yang Dong
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2020)
Article
Engineering, Electrical & Electronic
Lingling Sun, Jing Qiu, Wang Zhang, Ke Meng, Xia Yin, Zhaoyang Dong
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2020)
Proceedings Paper
Green & Sustainable Science & Technology
Vivienne Hui Fan, Ke Meng, Jing Qiu, Zhaoyang Dong
2020 4TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2020)
(2020)
Article
Engineering, Electrical & Electronic
Shuai Lu, Wei Gu, Cuo Zhang, Ke Meng, Zhaoyang Dong
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Computer Science, Information Systems
Yongxi Zhang, Ke Meng, Fengji Luo, Hongming Yang, Jiahua Zhu, Zhao Yang Dong
Article
Computer Science, Information Systems
Jing Zhang, Luqin Fan, Ying Zhang, Gang Yao, Peijia Yu, Guojiang Xiong, Ke Meng, Xiangping Chen, Zhaoyang Dong
