Recommender Systems Based on Evolutionary Computing: A Survey

Sadeghi, Maryam; Asghari, Seyyed Amir

doi:10.4236/jsea.2017.105023

Cited by 12 publications

(5 citation statements)

References 56 publications

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“…There are numerous applications for producing electricity from solar energy. The residential, transportation, aerospace, aviation, and maritime sectors are the main markets for solar energy [31] . The photoelectric effect is the cycle by which a photovoltaic cell transforms solar energy into usable electricity.…”

Section: Structure Of Voltage Control Systemmentioning

confidence: 99%

Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks

Abdullah Sami Assaf

2024

jes

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Given the growing emphasis on environmental consciousness, there is a spike in the integration of solar photovoltaic energy into contemporary distribution networks. Interestingly, there is a non-linear relationship between the energy production of solar modules and changing external environmental conditions. In response, this study aims to increase the effectiveness of solar photovoltaic systems (SPVS) by putting out a careful analysis to ascertain the ideal performance criteria for the integration of renewable energy sources into distribution networks. The use of a Maximum Power Point Tracker is crucial to this assessment (MPPT). The model provides important insights into the possible increases in energy efficiency, making it a strong tool in this optimization process. In addition, the research conducts a comparison analysis, examining the distribution system properties in relation to different levels of solar PV system penetration. This aspect of the research illuminates the consequences of harmonic-induced distortions in current and voltage on the feeder networks in the distribution system. The simulation findings clearly show that as the penetration capacity of the PV system increases, so does the amount of harmonic dispersion that is introduced into the network. This suggests that it would be wise to incorporate the photovoltaic (PV) array only as far as the network can support it in order to prevent any possible performance reduction. Using MATLAB/SIMULINK as a computational tool, the research carefully examines the important characteristics from the technical data in order to examine the overall model. Further testing of the model's flexibility and resilience under a range of weather scenarios and partial shade conditions offers a thorough assessment of the model's performance dynamics. Positively, the investigation concluded with results that were satisfactory and confirmed the system's exceptional performance capabilities, which are supported by the MPPT. This result reflects a positive trend toward maximizing the integration of renewable energy sources into distribution networks, which will help to create a more sustainable and environmentally friendly energy landscape.

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Section: Structure Of Voltage Control Systemmentioning

confidence: 99%

Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks

Abdullah Sami Assaf

2024

jes

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“…Raza and Ding [14] presented a review of recent progress, opportunities, and challenges in RSs. In addition, Sadeghi [19] discussed a survey relating to the use of the evolutionary computing and its application to RSs. Snasel et al [15] built a Boolean matrix factorization and GA-based RS method.…”

Section: Related Workmentioning

confidence: 99%

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Idhaim,

Kilani,

Alsarhan

et al. 2023

IEEE Access

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Due to the availability of massive numbers of items for any product on the Web, the burden of selecting an item is borne by the user. A Recommender System (RS) is a useful tool that has been employed to save the user's time by recommending preferred items for him/her efficiently. Collaborative-based RS's use the latent factor or/and the neighbourhood techniques including matrix factorization (MF), which is an efficient approach utilizing latent factors. The idea of the approach is based on calculating similarities through between users and items simultaneously, to predict appropriate recommendations. MF can be seen as mathematical model capable to split an entity into multiple smaller entries through an ordered rectangular array of functions, to discover the features or information underlying the interactions between users and items. The technique factorizes the user-item rating matrix (R) (first level factorization) into users (P) and items (Q) matrices. A recent RS system, named NLM, combines the neighbourhood and the latent factor techniques to produce attractive results. This paper proposes to improve the NLM method by proposing a more effective technique, named NLM+, by further factorizing P and Q using MF using a second level factorization. To the best of our knowledge, current studies in this field have not considered the use of the aforementioned two levels of factorizing. To validate our method, CiaoDVD, MovieLens, and FilmTrust datasets have been used and incorporated to demonstarte that NLM+ improves NLM by 48% and 42% in the recall and the precision values, respectively.

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“…Note that the only stop criterion of our proposal is to reach a maximum number of generations MAX iter = 150. The use of evolutionary algorithms along RSs is not new, as it is possible to find many papers that use this kind of algorithm to enhance RSs [17,18]. However, according to the surveys on that topic, most of the papers from the state of the art are devoted to treat the weights of both the features and the latent models as the individual of the evolutionary process.…”

Section: Operatormentioning

confidence: 99%

Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming

et al. 2020

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Recommender systems aim to estimate the judgment or opinion that a user might offer to an item. Matrix-factorization-based collaborative filtering typifies both users and items as vectors of factors inferred from item rating patterns. This method finds latent structure in the data, assuming that observations lie close to a low-dimensional latent space. However, matrix factorizations have been traditionally designed by hand. Here, we present Evolutionary Matrix Factorization (EMF), an evolutionary approach that automatically generates matrix factorizations aimed at improving the performance of recommender systems. Initial experiments using this approach show that EMF generally outperforms baseline methods when applied to MovieLens and FilmTrust datasets, having a similar performance to those baselines on the worst cases. These results serve as an incentive to continue improving and studying the application of an evolutionary approach to collaborative filtering based on Matrix Factorization.

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Recommender Systems Based on Evolutionary Computing: A Survey

Cited by 12 publications

References 56 publications

Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks

Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming

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