Prediction Of Different Types Research Articles

Energy Utilization Prediction Techniques for Heterogeneous Mobile Robots: A Review

The growing significance of mobile robots in a full spectrum of areas of life creates new challenges and opportunities in robotics. One critical aspect to consider is energy utilization, as accurate prediction plays a vital role in a robot’s reliability and safety. Furthermore, precise prediction offers economic advantages, particularly for robotic fleets, where energy management systems can optimize maintenance costs and operational efficiency. The following review describes the state of the art of energy usage prediction for different types of mobile robots, highlights current trends, and analyses algorithms’ complexity (in implementation and execution), accuracy, and universality.

Machine learning approach to predict flow fields induced by internal solitary waves acting on mid-water structures based on particle image velocimetry experiments

Mid-water structures (MWSs) have important applications in the ocean to minimize the effects of environmental factors in the surface ocean and to avoid the effects of high pressure on the seabed. However, their operational positions are vulnerable to internal solitary waves (ISWs), which poses a significant challenge to the safe functioning of MWSs. Consequently, there is a pressing need to explore the flow field characteristics surrounding MWSs under the action of ISWs to establish a reliable foundation for structural design. In this study, experimental investigations were conducted to analyze the flow fields induced by ISWs acting on MWSs, employing the particle image velocimetry (PIV) method. The intricate details of the flow fields around the structure were thoroughly analyzed, and the impact of the structure's submerged depth on the ISW-induced flow fields was discussed. A machine learning approach is utilized to develop an Artificial Neural Network (ANN) model aimed at predicting the flow fields of ISWs. The experimental results are compared and analyzed alongside the predicted outcomes, focusing on the anomaly region, the tail field region, and the occluded region. The results show that the presence of the structure complicates the flow characteristics in the tail flow regions, and the flow patterns are constantly changing. The characteristics of the ISW flow fields around the structure vary with the different submerged depths of the MWS. Positioning the MWS above the wave trough results in an ascending trend followed by a descending trend in the strength of the surrounding flow field with increasing submerged depth. The ANN predictions for different types of regions of the flow fields show satisfactory accuracy. The model closely matches the PIV results in predicting the extremes and positions of horizontal velocities in the tail flow fields at different stages of ISW trough propagation. The ANN model can be recommended for predicting the flow fields of ISWs acting on MWSs. This study offers comprehensive insights into the characteristics of the flow fields induced by ISWs acting on MWSs, emphasizing the valuable contribution of machine learning methods to the field of fluid dynamics.

Digital data and personality: A systematic review and meta-analysis of human perception and computer prediction.

In recent years, our increasing use of technology has resulted in the production of vast amounts of data. Consequently, many researchers have analyzed digital data in attempt to understand its relationship with individuals' personalities. Such endeavors have inspired efforts from divergent fields, resulting in widely dispersed findings that are seldom synthesized. In this two-part study, we draw from two distinct areas of personality prediction across psychology and computer science to explore the convergent validity of self-reports with human perception and machine learning algorithms, the identifiability of the Big Five traits, and the predictability of different types of data. In Study 1, five meta-analyses of human perception studies integrating findings from 24,124 individuals rated across 30 independent samples demonstrated moderate convergent validity across all traits (ranging from ρ = 0.38 for Neuroticism, to ρ = 0.57 for Openness). In Study 2, a multilevel meta-analysis of computer prediction studies reporting 534 effect sizes across 42 studies also demonstrated moderate convergent validity (ρ = 0.30). Multivariate analyses of the significant moderators highlighted that X, Facebook, Sina Weibo, videos, and smartphones had a negative impact on the variance identified. Finally, in synthesizing the extant literature, we discuss the measures used to assess personality and the analytical approaches adopted. We identify the strengths and limitations across each field and explain how interdisciplinary methodologies could advance the testing and development of psychological theory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improved prediction of DNA and RNA binding proteins with deep learning models.

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in essential biological processes. To facilitate functional annotation and accurate prediction of different types of NABPs, many machine learning-based computational approaches have been developed. However, the datasets used for training and testing as well as the prediction scopes in these studies have limited their applications. In this paper, we developed new strategies to overcome these limitations by generating more accurate and robust datasets and developing deep learning-based methods including both hierarchical and multi-class approaches to predict the types of NABPs for any given protein. The deep learning models employ two layers of convolutional neural network and one layer of long short-term memory. Our approaches outperform existing DBP and RBP predictors with a balanced prediction between DBPs and RBPs, and are more practically useful in identifying novel NABPs. The multi-class approach greatly improves the prediction accuracy of DBPs and RBPs, especially for the DBPs with ~12% improvement. Moreover, we explored the prediction accuracy of single-stranded DNA binding proteins and their effect on the overall prediction accuracy of NABP predictions.

Prediction of Different Types of Mobile Phone Prices based on Machine Learning Models

In contemporary society, mobile communication technology is advancing rapidly. People's lives have become more convenient, significantly driving the development of human society. As mobile communication introduces many new and practical technologies, people are increasingly inseparable from it. Mobile phones, as a form of mobile communication, have gradually integrated into people's lives and become a crucial and irreplaceable product. With the continuous popularization of smartphones and the increasing richness of mobile content, choosing a cost-effective phone has become a crucial decision for many. This article is based on hardware data and prices from a dataset comprising 2000 already-released smartphones. Machine learning techniques, specifically the K-Nearest Neighbors (KNN) model and linear regression model, are employed to predict the prices of these smartphones. The study reveals that both the K-Nearest Neighbors (KNN) model and the Linear Regression model can be utilized for predicting smartphone prices. However, the KNN model exhibits a slightly higher accuracy. Specifically, when K is set to 16, the accuracy of the KNN model reaches 93.3%. The Linear Regression model achieves an accuracy of 91.3%.

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

A Dual-Stage Attention-Based Vehicle Speed Prediction Model Considering Driver Heterogeneity with Fuel Consumption and Emissions Analysis

With the development of intelligent transportation systems (ITSs), personalized driving systems are receiving more and more attention, and the development of advanced systems cannot be separated from the practical exploration of drivers’ heterogeneous driving behaviors. An important foundation for subsequent driver-targeted research is how to mine the key influencing factors that characterize drivers through real driving data and how to appropriately classify drivers as a whole. This study took heterogeneous drivers as the object, based on a dual-stage attention-based vehicle speed prediction model, and carried out research on the speed prediction of traffic flow and the impact of fuel consumption and emissions in the car-following state considering the heterogeneity of drivers. Specifically, first, Spearman’s correlation analysis and K-means clustering were used to classify different types of drivers. Then, speed predictions for different types of drivers were separated via the dual-stage attention-based encoder–decoder (DAED) model and the prediction results between models and drivers were compared. Finally, the heterogeneous drivers’ fuel consumption and emissions were further analyzed via the VT-micro model. The results show that the proposed speed prediction model can effectively discriminate the influences of heterogeneous drivers on the prediction model, and the aggressive type presents the best effect. In addition, from the experiments on traffic fuel consumption and emissions, it can be concluded that the timid driver is the friendliest to the environment. By researching individual drivers’ driving characteristics, this study may help sustainable development in traffic management.

Modeling sorption of environmental organic chemicals from water to soils

Reliable estimation of chemical sorption from water to solid phases is an essential prerequisite for reasonable assessments of chemical hazards and risks. However, current fate and exposure models mostly rely on algorithms that lack the capability to quantify chemical sorption resulting from interactions with multiple soil constituents, including amorphous organic matter, carbonaceous organic matter, and mineral matter. Here, we introduce a novel, generic approach that explicitly combines the gravimetric composition of various solid constituents and poly-parameter linear free energy relationships to calculate the solid-water sorption coefficient (Kd) for non-ionizable or predominantly neutral organic chemicals with diverse properties in a neutral environment. Our approach demonstrates an overall statistical uncertainty of approximately 0.9 log units associated with predictions for different types of soil. By applying this approach to estimate the sorption of 70 diverse chemicals from water to two types of soils, we uncover that different chemicals predominantly exhibit sorption onto different soil constituents. Moreover, we provide mechanistic insights into the limitation of relying solely on organic carbon normalized sorption coefficient (KOC) in chemical hazard assessment, as the measured KOC can vary significantly across different soil types, and therefore, a universal cut-off threshold may not be appropriate. This research highlights the importance of considering chemical properties and multiple solid constituents in sorption modeling and offers a valuable theoretical approach for improved chemical hazard and exposure assessments.

Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening.

The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.

Improvement of an extended ensemble coupled data Assimilation–Forecast system and its application in El Niño diversity predictions

The El Niño–Southern Oscillation (ENSO) can cause climate anomalies on a global scale, and further affect human life and activities in coastal zones. Therefore, its forecast is of great significance for early disaster warning and coastal management. However, the frequent occurrence of central Pacific (CP) El Niño events increases the diversity and complexity of ENSO, severely reducing its prediction efficiency. In this study, an extended ensemble coupled data assimilation–forecast system was employed to investigate the prediction of different types of El Niño events, including eastern Pacific (EP) and CP events. The extended system was based on the fifth-generation Lamont–Doherty Earth Observation (LDEO5) model, in which an advanced ensemble Kalman filter was used to construct a multisource data assimilation system, and a stochastic optimal method was used to measure the influence of atmospheric stochastic processes on model prediction errors. The extended system was used to predict two types of El Niño events that occurred between January 1950 and December 2018. The results showed that the extended system was generally able to predict EP events with a higher accuracy than CP events for all lead times. The extended system successfully predicted the mature phase of EP events up to 12 months in advance but could only predict the mature phase of CP events up to 6 months in advance. The extended system was also able to depict the evolution of both EP and CP events, although the sea surface temperature anomalies were underestimated. The extended system not only provides a useful platform for improving ENSO prediction accuracies in association with El Niño diversity but also provides an important tool for disaster early warning and coastal management.

Extracting hydrothermally altered information using WorldView-3 data: a case study of Huitongshan, NW Gansu, China

Introduction: The Huitongshan skarn-type deposits, in which ore bodies primarily occur in the outer contact zone between K-feldspar granite and marble in the Beishan area, are evidently related to hydrothermal alteration of the surrounding rock. Key mineral alteration processes include serpentinization, epidotization, chloritization, carbonatization, jarosite, ferritization, and hematitization.Methods: WorldView-3 (WV-3), a satellite-recorded high-spatial resolution multispectral image, has been widely used in the exploration and prediction of different types of deposits around the world. In this study, WV-3 multispectral images were used to extract the spatial distribution data of the main altered minerals in the Huitongshan area. Dedicated radiometric calibration, atmospheric correction, and image fusion were used to pre-process the extracted spectral information related to hydrothermal alteration. In addition, directed principal component analysis (PCA) and a unique mineral index were designed based on the effective use of the WV-3 data band corresponding to the spectral absorption characteristics of altered minerals.Results: The findings of this study show that the PCA model and mineral index pro-posed herein are reliable both in theory and for practically obtaining extraction information. Additionally, the WV-3 data are well suited for identifying hydroxy-bearing alterations with rich short-wave infrared bands that distinguish Fe-OH–bearing alterations from Mg-OH–bearing alterations. The results obtained were applied to identify potential targets for skarn-type copper deposits and the implementation of prospecting practices.Discussion: This study provides a basis for the application of WV-3 data as an important and effective tool for alteration information extraction and determination of prospecting practice, thereby proving the validity of multispectral remote sensing images in mineral resource exploration.

Analysis of heartbeat time series via machine learning for detection of illnesses

The heart, a component of the cardiovascular system, is responsible for pumping oxygenated and deoxygenated blood. It does not behave like a metronome and normally there is a variation in the duration of the intervals between each heartbeat, called Heart Rate Variability (HRV). In the presence of diseases or with the progression of aging, there is a reduction in HRV due to dysfunction of the autonomic nervous system. The objective of this work is to show, using machine learning techniques, that these techniques are able to relate directly the variability of the heart with the degree of the disease. Producing, as a practical result, the use of these techniques in the prediction of different types of diseases by only analyzing their time series. One of the first techniques used in our work is the unsupervised learning algorithm (t-Stochastic Neighbor Embedding). We show that this algorithm is able to differentiate the type and degree of the disease just by analyzing time series, we demonstrate that it is possible to design a neural network architecture capable of learning these characteristics, relating cardiac variability and the disease. In a complementary analysis, we check that cardiac variability can be directly related to permutation entropy, proving that the healthier an individual is, the more stochastic his cardiac time series is. We build a classification algorithm, using deep learning, from the confusion matrix and the ROC curve. This algorithm can be used as an entry point in diagnosing patients by measuring their HRV.

Using a combination of peripapillary atrophy area and choroidal thickness for the prediction of different types of myopic maculopathy.

To analyse the topographic characteristics in macular choroidal thickness (mChT) and ocular biometry in myopic maculopathy and to explore the potential cut-off value for prediction of myopic maculopathy (MM). All participants underwent detailed ocular examinations. MM was subdivided into thin choroid, Bruch's membrane (BM) defects, choroidal neovascularization (CNV), and myopic tractional maculopathy (MTM) according to OCT-based classification system. Peripapillary atrophy area (PPA), tilt ratio, torsion, and mChT were individually measured. A total of 1947 participants were included. In multivariate logistics models, older age, longer axial length, larger PPA area, and thinner average mChT were more likely to have MM and different type of MM. Female participants were more likely to have MM and BM defects. A lower tilt ratio was more likely to be associated with CNV and MTM. The area under the curve (AUC) of single tilt ratio, PPA area, torsion, and topographic of mChT for MM, thin choroid, BM Defects, CNV, and MTM were 0.6581 to 0.9423, 0.6564 to 0.9335, 0.6120 to 0.9554, 0.5734 to 0.9312, 0.6415 to 0.9382, respectively. After combining PPA area and average mChT for predicting MM, thin choroid, BM defects, CNV, and MTM, the AUC of the combination were 0.9678, 0.9279, 0.9531, 0.9213, 0.9317, respectively. Progressive and continuous PPA area expanding and thin choroid play a role in the development of myopic maculopathy. The present study showed that a combination of peripapillary atrophy area and the choroidal thickness could be used to predict MM and each type of MM.

A multi-source transfer learning model based on LSTM and domain adaptation for building energy prediction

Transfer learning can use the knowledge learned from the operating data of other buildings to facilitate the energy prediction of a target building. However, most of the current research focuses on the transfer of a single source building of the same type or from the same region. A single source domain produces domain shift due to the difficulty of aligning the distribution with the target domain. To address this problem, this paper proposes a novel multi-source transfer learning energy prediction model based on long short-term memory (LSTM) and multi-kernel maximum mean difference (MK-MMD) domain adaptation. This model was used for the short-term energy prediction of different types of buildings lacking historical data. In addition, dynamic time warping (DTW) was used to select the source domain. Multiple multi-source models and corresponding single-source models were compared on a collection of buildings in the Higashida area of Fukuoka Prefecture, Japan. On the experimental datasets, the results showed that DTW relatively accurately measured the similarity between building energy consumption datasets. Compared with the results of the single-source transfer learning models, the multi-source transfer learning models achieved better average prediction performance, and their mean absolute percentage error (MAPE) improved the prediction accuracy by 6.88–15.37%.

A deep spatiotemporal approach in maritime accident prediction: A case study of the territorial sea of South Korea

Predicting the risk of maritime accidents is crucial for improving traffic surveillance and marine safety. The availability of data sources and development of machine learning and deep learning methodologies can improve operational risk prediction. Similar to larger vessels, numerous ocean accidents are caused by small- and medium-sized vessels owing to poor conditions and defective equipment. This study aims at investigating the application of deep learning in both short- and long-term predictions of different types of accident risks associated with small vessels by considering multiple influencing factors. Herein, several big data sources that contain data collected from the territorial sea of South Korea, including ocean accidents, ocean depth, weather data, and small vessel trajectories, are utilized. Four machine learning and six deep learning algorithms are implemented and compared in nine scenarios with three different grid sizes using daily, weekly, and monthly models. The results reveal that although the performance of the proposed deep spatiotemporal ocean accident prediction (DSTOAP) model varies according to grid sizes and time intervals, its accuracy (more than 78%) makes it reliable for predicting accidents. Furthermore, although all types of accidents are captured with high accuracy, more than 84% of collision accidents can be predicted accurately. For practical applications, the results of this study can guide ocean accident management and safety planners in choosing appropriate methods for different time schedules and grid sizes, according to the range of coverage of the patrol ship.

Using Explainable AI (XAI) for the Prediction of Falls in the Older Population

The prevention of falls in older people requires the identification of the most important risk factors. Frailty is associated with risk of falls, but not all falls are of the same nature. In this work, we utilised data from The Irish Longitudinal Study on Ageing to implement Random Forests and Explainable Artificial Intelligence (XAI) techniques for the prediction of different types of falls and analysed their contributory factors using 46 input features that included those of a previously investigated frailty index. Data of participants aged 65 years and older were fed into four random forest models (all falls or syncope, simple fall, complex fall, and syncope). Feature importance rankings were based on mean decrease in impurity, and Shapley additive explanations values were calculated and visualised. Female sex and a previous fall were found to be of high importance in all of the models, and polypharmacy (being on five or more regular medications) was ranked high in the syncope model. The more ‘accidental’ (extrinsic) nature of simple falls was demonstrated in its model, where the presence of many frailty features had negative model contributions. Our results highlight that falls in older people are heterogenous and XAI can provide new insights to help their prevention.

Permeability change with respect to different hydrate saturation in clayey-silty sediments

The successful pilot trial of methane hydrate production in the South China Sea has proven the technical feasibility of natural gas hydrates (NGHs) exploitation in clayey-silty reservoirs. As a key issue of NGHs exploitation in clayey-silty sediments, the variation of permeability with hydrate saturation (Sh) has not been thoroughly studied. In this study, a triaxial core holder was designed to simulate the synthesis and dissociation of gas hydrates in a clayey-silty core plug sample. In the process of hydrate synthesis and multi-step dissociation, the water content and distribution in different pores were quantitatively determined by a nuclear magnetic resonance (NMR) spectrometer, and the variation of water content under different Sh was successively monitored. Meanwhile, in each depressurization phase, the gas permeabilities k(Sh) with respect to different Sh were measured under constant effective stress. Results highlight that the change of k(Sh) is more prevalent in clayey-silty sediments than that in sandstones. As Sh increases from 0 to 10.43%, the permeability ratio (k(Sh) to sedimentary permeability ks) in clayey-silty sediments decreases to 0.01–0.02; whereas in sandstones, the permeability ratio falls between 0.4 and 0.8. To predicate the permeability change in clayey-silty sediments, the previous Cubic model is improved and a Power-exponential model is presented. In the updated model, the variable of power parameter n can characterize different reservoir types. Specifically, in artificial sandstones, sandpacks or sandy reservoirs, the n values range from 3.1 to 8.3, while in clay packs or clayey-silty sediments, the n values are from 19.1 to 30.7. The Power-exponential model provides reference values for the prediction of different types of gas hydrate reservoirs such as permafrost and marine sediments.

Population inference of spin-induced quadrupole moments as a probe for nonblack hole compact binaries

Gravitational wave (GW) measurements of physical effects such as spin-induced quadrupole moments can distinguish binaries consisting of black holes from nonblack hole binaries. While these effects may be poorly constrained for single-event inferences with the second-generation detectors, combining information from multiple detections can help uncover features of nonblack hole binaries. The spin-induced quadrupole moment has specific predictions for different types of compact objects, and a generalized formalism must consider a population where different types of compact objects co-exist. In this study, we introduce a hierarchical mixture-likelihood formalism to estimate the fraction of nonbinary black holes in the population. We demonstrate the applicability of this method using simulated GW signals injected into Gaussian noise following the design sensitivities of the Advanced LIGO Advanced Virgo detectors. We compare the performance of this method with a traditionally-followed hierarchical inference approach. Both the methods are equally effective to hint at inhomogeneous populations, however, we find the mixture-likelihood approach to be more natural for mixture populations comprising compact objects of diverse classes. We also discuss the possible systematics in the mixture-likelihood approach, caused by several reasons, including the limited sensitivity of the second-generation detectors, specific features of the astrophysical population distributions, and the limitations posed by the waveform models employed. Finally, we apply this method to the LIGO-Virgo detections published in the second GW transient catalog (GWTC-2) and find them consistent with a binary black hole population with ${f}_{\mathrm{nbh}}$ estimated to be $\ensuremath{\le}0.29$ at 90% credibility.

Identification of intimate partner violence from free text descriptions in social media

Intimate partner violence (IPV) is a significant public health problem that adversely affects the well-being of victims. IPV is often under-reported and non-physical forms of violence may not be recognized as IPV, even by victims. With the increasing popularity of social media and due to the anonymity provided by some of these platforms, people feel comfortable sharing descriptions of their relationship problems in social media. The content generated in these platforms can be useful in identifying IPV and characterizing the prevalence, causes, consequences, and correlates of IPV in broad populations. However, these descriptions are in the form of free text and no corpus of labeled data is available to perform large-scale computational and statistical analyses. Here, we use data from established questionnaires that are used to collect self-report data on IPV to train machine learning models to predict IPV from free text. Using Universal Sentence Encoder (USE) along with multiple machine learning algorithms (random forest, SVM, logistic regression, Naïve Bayes), we develop DetectIPV, a tool for detecting IPV in free text. Using DetectIPV, we comprehensively characterize the predictability of different types of violence (physical abuse, emotional abuse, sexual abuse) from free text. Our results show that a general model that is trained using examples of all violence types can identify IPV from free text with area under the ROC curve (AUROC) 89%. We also train type-specific models and observe that physical abuse can be identified with greatest accuracy (AUROC 98%), while sexual abuse can be identified with high precision but relatively low recall. While our results indicate that the prediction of emotional abuse is the most challenging, DetectIPV can identify emotional abuse with AUROC above 80%. These results establish DetectIPV as a tool that can be used to reliably detect IPV in the context of various applications, ranging from flagging social media posts to detecting IPV in large text corpuses for research purposes. DetectIPV is available as a web service at https://www.ipvlab.case.edu/ipvdetect/.

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset

Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper seeks the origin of these biases and unfairness. To this end, it analyzes the demographic characteristics of a gold standard dataset and its prediction performance when used in a multitude of Recommender Systems. In addition, this paper proposes Soft Matrix Factorization (SoftMF), which tries to balance the predictions of different types of users to reduce the present inequality. The experimental results show that those biases and unfairness are not introduced by the different recommendation models and that they come from the socio-psychological and demographic characteristics of the used dataset.