scholarly journals FRIEND RECOMMENDATION USING GRAPH MINING ON SOCIAL MEDIA

2020 ◽  
Vol 4 (5) ◽  
pp. 57-65
Author(s):  
Naren Kumar Kosaraju ◽  
Vineela Kanakamedala
Keyword(s):  
Social Media ◽  
Graph Mining ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Similarity Measures ◽  
Huge Amount ◽  
Custom Made ◽  
The Impact

Recommendation system is an important type of machine learning algorithm that provide precise suggestions to the users. Recommendation systems are used in innumerable types of areas such as generation of playlists, music and video services like Jio savaan, wynk, amazon prime music etc., and products recommendation for users in e-commerce applications and commercial applications. The recommendations that are provided by various types of applications increases the speed for identifying and makes easier to access the products that users are interested in. For each user, the recommendation system is capable of envisaging the future predilections on a set of items and recommend the top items. In several industries, recommendation systems are very useful as they generate huge amount of income and this type of industries can stand uniquely from competitors. Due to cumbersome number of items that each user can find in the web, the impact of recommendation system has been increased in the internet. Recommendation systems are used for custom-made navigation by getting huge amount of data particularly in social media domain for recommending friends. A recommendation system act as a subclass for the information filtering system that pursue to predict the rating. The similarity measures that are calculated in this research are Jaccard distance and Otsuka-Ochiai coefficient. The feature extractions that are used in this paper are Adar index, PageRank, Katz centrality, Hits score. Now a days many research people are implementing different types of algorithms in various domains for recommendation systems.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Search ◽  
Recommendation System ◽  
Opinion Mining ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Content Type ◽  
The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

scholarly journals The Impact of Recommendation System Overuse on the Subjective Wellbeing of Internet Users

2021 ◽  
pp. 239-247
Author(s):  
Morrant Hemans ◽  
Dickson Kofi Wiredu Ocansey
Keyword(s):  
Social Media ◽  
Social Network ◽  
Subjective Wellbeing ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Research Data ◽  
Self Concept ◽  
The Internet ◽  
Internet Users ◽  
The Impact

The stress in identifying useful information on the internet facilitated the use of recommendation systems by e-commerce and social network platforms to help users find their interested information quickly. However, this study seeks to investigate the impact of recommendation system overuse on the subjective wellbeing of internet users. The study reviewed previous studies in developing a research framework from the SOBC model that identifies social media overload as a (Situation) that triggers the intentions of internet users (Organism) to excessively use recommendation systems on the internet based on their level of self-concept clarity (Behavior) which (Consequently) affect their subjective wellbeing. SmartPLS 3.0 and SPSS v.22 software were deployed to analyze the research data and discover links between the constructs. The obtained results of the study confirm that, overuse of online recommendation systems negatively affect users’ subjective wellbeing.

scholarly journals Sentiment Analysis of Events on Social Web

2020 ◽  
Vol 9 (4) ◽  
pp. 1232-1238
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Extraction Process ◽  
Online Data ◽  
Promising Area ◽  
Data Volume ◽  
Application Data

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.

scholarly journals CourseQ: the impact of visual and interactive course recommendation in university environments

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Boxuan Ma ◽  
Min Lu ◽  
Yuta Taniguchi ◽  
Shin’ichi Konomi
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
User Study ◽  
Recommendation Systems ◽  
User Interaction ◽  
Design Concepts ◽  
System A ◽  
Accuracy Of Prediction ◽  
The Impact ◽  
Academic Interests

AbstractThe abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

scholarly journals A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Recommendation System into Social Life

2020 ◽  
Vol 10 (5) ◽  
pp. 37-39
Author(s):  
Shawni Dutta ◽  
Prof. Samir Kumar Bandyopadhyay
Keyword(s):  
Social Science ◽  
Recommender System ◽  
Medical Diagnosis ◽  
Social Life ◽  
Recommendation System ◽  
Information Filtering ◽  
Medical Science ◽  
Recommendation Systems ◽  
Basic Task ◽  
System Recommendation

Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the "rating" or "preference" of a user to an item.  In other words, both predict rating or preference for an item or product on a specific platform. The aim of the paper is to extend the areas of the recommender system/recommendation systems. The basic task of the recommender system mainly is to predict or analyze items/product. If it is possible to include more products in the system, then obviously the system may be extended for other areas also. For example, Medicine is a product and doctors filter the particular medicine for the particular disease. In the medical diagnosis doctors prescribed a medicine and it a product. It depends on the disease of the user/patient so here doctor predicts a medicine or product just like an item is recommended in a recommender system. The main objective of the paper is to extend the Recommender System/Recommendation system in other fields so that the research works can be extended Social Science, Bio-medical Science and many other areas.

scholarly journals Recommendation Systems in the Big Data Era

2020 ◽  
Vol 8 (12s3) ◽  
pp. 80-85
Keyword(s):  
Social Media ◽  
Big Data ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Rapid Progression ◽  
Specific Product ◽  
Hybrid Approaches ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
The Way

Rapid progression in technology and increasing use of social media platforms like Facebook, Instagram and Twitter has altered the way of articulating people’s judgment, observation and sentiments about specific product, services, and more. This leads to the production and accumulation of massive amount of data. Recommendation systems are getting impetus when it comes to find insights from this data to make decisions that can be represented in various statistical and graphical forms. They have proven useful in predicting or recommending products ranging from food, movies, restaurants etc. This paper presents an overview about recommendation systems and a review of generation of recommendation methods based on categories like contentbased, collaborative, and hybrid approaches. The paper will enlist the limitations which the present recommendation system faces and the possible improvements required in their capabilities to fit into a wider range of application areas.

scholarly journals A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

2019 ◽  
Vol 16 (1(Suppl.)) ◽  
pp. 0263
Author(s):  
AL-Bakri Et al.
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Similarity Measures ◽  
Huge Amount ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Target User ◽  
Global Meaning ◽  
System Memory ◽  
Accuracy Of Prediction

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

scholarly journals Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF

2021 ◽  
Vol 9 (9) ◽  
pp. 181-191
Author(s):  
S. A. Azeem Farhan
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Value Decomposition

Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

scholarly journals Skype, Code and Shouting: A Digitally Mediated Drama between Egypt and Scotland

Leonardo ◽  
2015 ◽  
Vol 48 (3) ◽  
pp. 284-285
Author(s):  
Shabina Aslam ◽  
Eleanor Dare
Keyword(s):  
Social Media ◽  
Ethnic Minority ◽  
Arab Spring ◽  
Final Performance ◽  
Custom Made ◽  
Theatrical Performance ◽  
Theatre Company ◽  
The Arab Spring ◽  
The Impact

Springtime (Tron Theatre, Glasgow, 19 May 2012) was a computationally mediated theatrical performance involving Arab and Glaswegian-Arab actors and musicians. The project was produced by Ankur Theatre Productions, Scotland’s foremost black and ethnic minority theatre company. Springtime was directed by the dramaturge Shabina Aslam. Against the backdrop of the “Arab Spring” and its aftermath, the play explored issues of authenticity and identity as mediated through multiple technologies. This paper explores the impact and significance of the production and evaluates the use of Skype, social media and custom-made software in the writing, rehearsal and final performance stages of the play.

scholarly journals Hoax News Classification using Machine Learning Algorithms

2019 ◽  
Vol 9 (2) ◽  
pp. 3938-3944
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
The Impact ◽  
F Measure

Hoax news on social media has had a dramatic effect on our society in recent years. The impact of hoax news felt by many people, anxiety, financial loss, and loss of the right name. Therefore we need a detection system that can help reduce hoax news on social media. Hoax news classification is one of the stages in the construction of a hoax news detection system, and this unsupervised learning algorithm becomes a method for creating hoax news datasets, machine learning tools for data processing, and text processing for detecting data. The next will produce a classification of a hoax or not a Hoax based on the text inputted. Hoax news classification in this study uses five algorithms, namely Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Stochastic Gradient Descent, and Neural Network (MLP). These five algorithms to produce the best algorithm that can use to detect hoax news, with the highest parameters, accuracy, F-measure, Precision, and recall. From the results of testing conducted on five classification algorithms produced shows that the NN-MPL algorithm has an average of 93% for the value of accuracy, F-Measure, and Precision, the highest compared to five other algorithms, but for the highest Recall value generated from the algorithm SVM which is 94%. the results of this experiment show that different effects for different classifiers, and that means that the more hoax data used as training data, the more accurate the system calculates accuracy in more detail.

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