scholarly journals Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

2019 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Will Serrano
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Recommender Systems ◽  
Recommender System ◽  
Commercial Interest ◽  
Big Data Applications ◽  
Random Neural Network

Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.

scholarly journals Contextual Personality-Aware Recommender System Versus Big Data Recommender System

2021 ◽  
pp. 163-173
Author(s):  
Marcin Szmydt
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Factor Model ◽  
Five Factor Model ◽  
Customer Reviews ◽  
System Versus ◽  
Personality Theories

Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

2021 ◽  
Author(s):  
Nour Salim Nassar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Deep Neural Network ◽  
Recommendation Systems ◽  
The Other ◽  
Learning Techniques ◽  
Novel Model

Abstract Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

scholarly journals Recommending Tasks in Online Judges using Autoencoder Neural Networks

2020 ◽  
pp. 61-76
Author(s):  
Paolo FANTOZZI ◽  
Luigi LAURA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Recommender Systems ◽  
Recommender System ◽  
Experimental Evaluation ◽  
Large Set ◽  
Dynamic Dimension ◽  
Artificial Neural Network Ann ◽  
Intrinsic Dynamic

Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. To train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.

scholarly journals A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews

2021 ◽  
Vol 13 (6) ◽  
pp. 25-39
Author(s):  
Nazia Tazeen ◽  
◽  
K. Sandhya Rani
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fitness Function ◽  
Computation Time ◽  
High Accuracy ◽  
Sentiment Classification ◽  
Big Data Applications ◽  
Classification Tasks ◽  
Research Drug ◽  
Drug Review

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks.Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colonybased Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.

scholarly journals Applying Neural Network Model to Hybrid Tourist Attraction Recommendations

2014 ◽  
Vol 6 (2) ◽  
pp. 63-69
Author(s):  
Marcelli Indriana ◽  
Chein-Shung Hwang
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Recommender System ◽  
Training Dataset ◽  
Target Value ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering

Recently, recommender systems have been developed for a variety of domains. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative filtering (CF), and Demographic filtering (DF). However, the uses of these techniques individually will have some disadvantages. In this research, we propose a hybrid recommender system to combine the predictions from CB, CF and DF approaches using neural network model. Neural network model will learn by processing a training dataset, comparing the network’s prediction for each dataset with the actual known target value. For each training dataset, the weights are modified to minimize the mean-squared error between the network’s prediction and the actual target value. The experimental results showed that the neural network model outperforms each individual recommendation techniques. Index Terms - Colaborative Filtering, Content-based filtering, Data Mining, Demographic Filtering, Hybrid Recommender System, Neural Network

scholarly journals Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fezzeh Ebrahimi ◽  
Asefeh Asemi ◽  
Amin Nezarat ◽  
Andrea Ko
Keyword(s):  
Mathematical Model ◽  
Big Data ◽  
Recommender System ◽  
Graph Mining ◽  
Data Science ◽  
System Model ◽  
Expert Opinions ◽  
Big Data Applications ◽  
Pubmed Database ◽  
Correlation Degree

AbstractFinding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present study aims at designing a mathematical model of co-author recommender system in bioinformatics using graph mining techniques and big data applications. The present study employed an applied-developmental research method and a mixed-methods research design. The research population consisted of all scientific products in bioinformatics in the PubMed database. To achieve the research objectives, the most appropriate effective features in choosing a co-author were selected, prioritized, and weighted by experts. Then, they were weighted using graph mining techniques and big data applications. Finally, the mathematical co-author recommender system model in bioinformatics was presented. Data analysis instruments included Expert Choice, Excel, Spark, Scala and Python programming languages in a big data server. The research was conducted in four steps: (1) identifying and prioritizing the criteria effective in choosing a co-author using AHP; (2) determining the correlation degree of articles based on the criteria obtained from step 1 using algorithms and big data applications; (3) developing a mathematical co-author recommender system model; and (4) evaluating the developed mathematical model. Findings showed that the journal titles and citations criteria have the highest weight while the abstract has the lowest weight in the mathematical co-author recommender system model. The accuracy of the proposed model was 72.26. It was concluded that using content-based features and expert opinions have high potentials in recommending the most appropriate co-authors. It is expected that the proposed co-author recommender system model can provide appropriate recommendations for choosing co-authors on various fields in different contexts of scientific information. The most important innovation of this model is the use of a combination of expert opinions and systemic weights, which can accelerate the finding of co-authors and consequently saving time and achieving a greater quality of scientific products.

scholarly journals Developing a Prediction Model for Author Collaboration in Bioinformatics Research Using Graph Mining Techniques and Big Data Applications

2020 ◽  
Author(s):  
Fezzeh Ebrahimi ◽  
Asefeh Asemi ◽  
Ahmad Shabani ◽  
Amin Nezarat
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Programming Languages ◽  
Recommender System ◽  
Communication Systems ◽  
Graph Mining ◽  
Research Output ◽  
Similarity Measures ◽  
Journal Title ◽  
Big Data Applications

Abstract Nowadays, scientific collaboration has dramatically increased as a result of web-based technologies, advanced communication systems, and development of information and scientific databases. The present study aims to provide a predictive model for author collaborations in bioinformatics research output using graph mining techniques and big data applications. The study is an applied-developmental research adopting a mixed-method approach, i.e. a mix of quantitative and qualitative measures. The research population consisted of all bioinformatics research documents indexed in PubMed (n = 699160). The correlations of bioinformatics articles were examined in terms of weight and strength based on article sections including title, abstract, keywords, journal title, and author affiliation using graph mining techniques and big data applications. Eventually, the prediction model of author collaboration in bioinformatics research was developed using the abovementioned tools and expert-assigned weights. The calculations and data analysis were carried out using Expert Choice, Excel, Spark as well as Scala and Python programming languages in a big data server. Accordingly, the research was conducted in three phases: 1) identifying and weighting the factors contributing to authors’ similarity measurement; 2) implementing co-authorship prediction model; and 3) integrating the first and second phases (i.e. integrating the weights obtained in the previous phases). The results showed that journal title, citation, article title, author affiliation, keywords, and abstract scored 0.374, 0.374, 0.091, 0.075, 0.055, and 0.031, respectively. Moreover, the journal title achieved the highest score in the model for the co-author recommender system. As the data in bibliometric information networks is static, it was proved remarkably effective to use content-based features for similarity measures so that the recommender system can offer the best and most suitable collaboration suggestions. It is expected that the model work efficiently in other databases and provide suitable recommendations for author collaborations in other subject areas. By integrating expert opinion and systemic weights, the model can help alleviate the contemporary information overload and facilitate collaborator lookup by authors.

Sign in / Sign up

Export Citation Format

Share Document