scholarly journals DSSMFM: Combining user and item feature interactions for recommendation systems

2020 ◽  
Vol 309 ◽  
pp. 03010
Author(s):  
Weishan Zeng
Keyword(s):  
Machine Learning ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Model Data ◽  
New Model ◽  
Feature Interactions ◽  
Short Video ◽  
Item Attributes ◽  
Relationship Of ◽  
The Relationship

Effort has been done to optimize machine learning algorithms by applying relevant knowledges in data fields in recommendation systems. Ways are explored to discover the relationship of features independently, making the model more effective and robust. A new model, DSSMFM is proposed in this paper which combines user and item features interactions to improve the performance of recommendation systems. In this model, data are divided into user features and item features represented by one-hot vectors. The pre-training for the model is proceeded through FM, and implicit vectors are obtained for both user and item features. The implicit vectors are used as the input of DSSM, and the training of the DSSM part of the model will maximize the cosine distances of the user attributes vectors and the item attributes vectors. According to the experimental results on dataset of ICME 2019 Short Video Understanding and Recommendation Challenge, the model shows improvements on some results of the baselines.

Employee Strengths Use and Thriving at Work

2020 ◽  
Vol 19 (4) ◽  
pp. 197-205
Author(s):  
He Ding ◽  
Xixi Chu
Keyword(s):  
Self Efficacy ◽  
Mediation Model ◽  
Model Data ◽  
Strengths Use ◽  
Moderated Mediation Model ◽  
Indirect Relationship ◽  
Relationship Of ◽  
The Relationship ◽  
Humble Leadership ◽  
Thriving At Work

Abstract. This study aimed to investigate the relationship of employee strengths use with thriving at work by proposing a moderated mediation model. Data were collected at two time points, spaced by a 2-week interval. A total of 260 medical staff completed strengths use, perceived humble leadership, self-efficacy, and thriving scales. The results of path analysis showed that strengths use is positively related to thriving, and self-efficacy mediates the relationship of strengths use with thriving. In addition, this study also found perceived humble leadership to positively moderate the direct relationship of strengths use with self-efficacy and the indirect relationship of strengths use with thriving via self-efficacy. This study contributes to a better understanding of how and when strengths use affects thriving.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

scholarly journals Edusemiotics as process semiotics: Towards a new model of semiosis for teaching and learning

Semiotica ◽  
2016 ◽  
Vol 2016 (212) ◽  
pp. 45-57 ◽  
Author(s):  
Andrew Stables
Keyword(s):  
Teaching And Learning ◽  
New Model ◽  
Relationship Of ◽  
The Relationship

AbstractStandard definitions posit the sign as a discrete entity in relation with other signs and standing for an object (either physical or psychological). Thus the sign has two roles, as prompt and as substitutive representation. The latter raises difficult questions about the relationship of the semiotic to the non-semiotic or pre-semiotic, which can be resolved logically (as in Peirce) or rejected as unanswerable (as in Saussure), but which can never be satisfactorily resolved empirically as the phenomenal cannot be divorced from the semiotic. This impasse can be resolved if we drop the assumption that the sign is essentially substitutive. The assumption of discrete entities, at either the phenomenal or the noumenal levels, is a function of discredited substance metaphysics. On a process metaphysical account, the reality of the sign is not attached to the discreteness of any pre-existing entity. The sign remains as prompt and as relational but not (other than sometimes with respect to other signs) substitutive. Rather than defined as standing for an object, the sign can now be regarded much more simply as a feature of an event. This conception of the sign is explored in terms of its implications for teaching and learning.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

scholarly journals Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1096
Author(s):  
Zeqiang Chen ◽  
Xin Lin ◽  
Chang Xiong ◽  
Nengcheng Chen
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Jinsha River ◽  
Precipitation Water ◽  
Relationship Of ◽  
The Relationship

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

PREDICTING SOFTWARE CHANGE IN AN OPEN SOURCE SOFTWARE USING MACHINE LEARNING ALGORITHMS

2013 ◽  
Vol 20 (06) ◽  
pp. 1350025
Author(s):  
RUCHIKA MALHOTRA ◽  
ANKITA JAIN BANSAL
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Roc Analysis ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Preventive Actions ◽  
Change Proneness ◽  
The Relationship

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.

scholarly journals Learning Predictors from Multidimensional Data with Tensor Factorizations

2021 ◽  
Vol 1 (3) ◽  
Author(s):  
Soo Min Kwon ◽  
Anand D. Sarwate
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
Tensor Structure ◽  
Statistical Machine Learning ◽  
Machine Learning Classification ◽  
Independent Variables ◽  
Tensor Factorizations ◽  
The Relationship

Statistical machine learning algorithms often involve learning a linear relationship between dependent and independent variables. This relationship is modeled as a vector of numerical values, commonly referred to as weights or predictors. These weights allow us to make predictions, and the quality of these weights influence the accuracy of our predictions. However, when the dependent variable inherently possesses a more complex, multidimensional structure, it becomes increasingly difficult to model the relationship with a vector. In this paper, we address this issue by investigating machine learning classification algorithms with multidimensional (tensor) structure. By imposing tensor factorizations on the predictors, we can better model the relationship, as the predictors would take the form of the data in question. We empirically show that our approach works more efficiently than the traditional machine learning method when the data possesses both an exact and an approximate tensor structure. Additionally, we show that estimating predictors with these factorizations also allow us to solve for fewer parameters, making computation more feasible for multidimensional data.

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