scholarly journals Implementation of Movie Recommendation System Using Machine Learning

2020 ◽  
pp. 587-593
Author(s):  
S. Sridevi ◽  
Celeste Murnal
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Recommendation System ◽  
Service Providers ◽  
Learning Algorithm ◽  
Web Based ◽  
Ensemble Learning Algorithm ◽  
Movie Recommendation ◽  
High Possibility ◽  
Content Based Filtering

As world is evolving, similarly people's desire, trend, interests are also changing. Same way even in the field of movies, people want to watch the movies according to their interest. Many web-based movie service providers have emerged and to increase their business and popularity, they want to keep their subscribers entertained. To improve their business, the service provider should recommend movies which users might like, so that they might watch another movie and be entertained. By doing this there is high possibility that customers will periodically renew the web-based movie service provider application. The objective of this project is to implement the machine learning based movie recommendation system which can recommend the movies to the users based on their interest and ratings. To achieve this, content-based filtering is used to recommend movie based on movie-movie similarity, collaborative based filtering is used to compute features based on user information and movie information. The proposed system uses the new ensemble learning algorithm, XGBoost algorithm to improve the performance. The results show that the proposed system is effective for movie recommendation and the system minimizes the root mean square error (RMSE).

scholarly journals The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services

2020 ◽  
Vol 10 (4) ◽  
pp. 5-16
Author(s):  
V.A. Sudakov ◽  
I.A. Trofimov
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Goods And Services ◽  
Complex Models ◽  
Combined Algorithm ◽  
Content Based Filtering

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

A Particle Swarm Optimization based Hybrid Recommendation System

2016 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Rabi Narayan Behera ◽  
Sujata Dash
Keyword(s):  
Recommendation System ◽  
Optimization Technique ◽  
User Profile ◽  
Matching Algorithm ◽  
Profile Matching ◽  
Machine Learning Approach ◽  
Movie Recommendation ◽  
Hybrid Recommendation ◽  
Content Based Filtering ◽  
Fine Tune

Due to rapid digital explosion user shows interest towards finding suggestions regarding a particular topic before taking any decision. Nowadays, a movie recommendation system is an upcoming area which suggests movies based on user profile. Many researchers working on supervised or semi-supervised ensemble based machine learning approach for matching more appropriate profiles and suggest related movies. In this paper a hybrid recommendation system is proposed which includes both collaborative and content based filtering to design a profile matching algorithm. A nature inspired Particle Swam Optimization technique is applied to fine tune the profile matching algorithm by assigning to multiple agents or particle with some initial random guess. The accuracy of the model will be judged comparing with Genetic algorithm.

scholarly journals A WEB-BASED FAST AND RELIABLE TEXT CLASSIFICATION TOOL

2019 ◽  
Vol 1 ◽  
pp. 24
Author(s):  
Jānis Kapenieks
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Storage ◽  
Text Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Web Based ◽  
Opinion Analysis ◽  
Classification Tool ◽  
User Friendly

INTRODUCTION Opinion analysis in the big data analysis context has been a hot topic in science and the business world recently. Social media has become a key data source for opinions generating a large amount of data every day providing content for further analysis. In the Big data age, unstructured data classification is one of the key tools for fast and reliable content analysis. I expect significant growth in the demand for content classification services in the nearest future. There are many online text classification tools available providing limited functionality -such as automated text classification in predefined categories and sentiment analysis based on a pre-trained machine learning algorithm. The limited functionality does not provide tools such as data mining support and/or a machine learning algorithm training interface. There are a limited number of tools available providing the whole sets of tools required for text classification, i.e. this includes all the steps starting from data mining till building a machine learning algorithm and applying it to a data stream from a social network source. My goal is to create a tool able to generate a classified text stream directly from social media with a user friendly set-up interface. METHODS AND MATERIALS The text classification tool will have a core based modular structure (each module providing certain functionality) so the system can be scaled in terms of technology and functionality. The tool will be built on open source libraries and programming languages running on a Linux OS based server. The tool will be based on three key components: frontend, backend and data storage as described below: backend: Python and Nodejs programming language with machine learning and text filtering libraries: TensorFlow, and Keras, for data storage Mysql 5.7/8 will be used, frontend will be based on web technologies built using PHP and Javascript. EXPECTED RESULTS The expected result of my work is a web-based text classification tool for opinion analysis using data streams from social media. The tool will provide a user friendly interface for data collection, algorithm selection, machine learning algorithm setup and training. Multiple text classification algorithms will be available as listed below: Linear SVM Random Forest Multinomial Naive Bayes Bernoulli Naive Bayes Ridge Regressio Perceptron Passive Aggressive Classifier Deep machine learning algorithm. System users will be able to identify the most effective algorithm for their text classification task and compare them based on their accuracy. The architecture of the text classification tool will be based on a frontend interface and backend services. The frontend interface will provide all the tools the system user will be interacting with the system. This includes setting up data collection streams from multiple social networks and allocating them to pre-specified channels based on keywords. Data from each channel can be classified and assigned to a pre-defined cluster. The tool will provide a training interface for machine learning algorithms. This text classification tool is currently in active development for a client with planned testing and implementation in April 2019.

scholarly journals UniAssist- Implementation of Machine Learning Based Higher Education University Recommendation System

2020 ◽  
pp. 262-266
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Work Experience ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Relevant Information ◽  
Mobile App ◽  
Admission Process ◽  
Software Application ◽  
Tuition Fee

UniAssist project is implemented to help students who have completed their Bachelorette degree and are looking forward to study abroad to pursue their higher education such as Masters. Machine Learning would help identify appropriate Universities for such students and suggest them accordingly. UniAssist would help such individuals by recommending those Universities according to their preference of course, country and considering their grades, work experience and qualifications. There is a need for students hoping to pursue higher education outside India to get to know about proper universities. Data collected is then converted into relevant information that is currently not easily available such as courses offered by their dream universities, the avg. tuition fee and even the avg. expense of living near the chosen university on single mobile app based software platform. This is the first phase of the admission process for every student. The machine-learning algorithm used is Collaborative filtering memory-based approach using KNN calculated using cosine similarity. A mobile-based software application is implemented in order to help and guide students for their higher education.

scholarly journals Exploring the Transition to Fatherhood: Feasibility Study Using Social Media and Machine Learning (Preprint)

2018 ◽  
Author(s):  
Samantha J Teague ◽  
Adrian BR Shatte
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Transition To Parenthood ◽  
Learning Algorithm ◽  
Clinical Care ◽  
Group Discussion ◽  
Web Based ◽  
Additional Tool ◽  
Discussion Threads ◽  
Rich Data

BACKGROUND Fathers’ experiences across the transition to parenthood are underreported in the literature. Social media offers the potential to capture fathers’ experiences in real time and at scale while also removing the barriers that fathers typically face in participating in research and clinical care. OBJECTIVE This study aimed to assess the feasibility of using social media data to map the discussion topics of fathers across the fatherhood transition. METHODS Discussion threads from two Web-based parenting communities, r/Daddit and r/PreDaddit from the social media platform Reddit, were collected over a 2-week period, resulting in 1980 discussion threads contributed to by 5853 unique users. An unsupervised machine learning algorithm was then implemented to group discussion threads into topics within each community and across a combined collection of all discussion threads. RESULTS Results demonstrated that men use Web-based communities to share the joys and challenges of the fatherhood experience. Minimal overlap in discussions was found between the 2 communities, indicating that distinct conversations are held on each forum. A range of social support techniques was demonstrated, with conversations characterized by encouragement, humor, and experience-based advice. CONCLUSIONS This study demonstrates that rich data on fathers’ experiences can be sourced from social media and analyzed rapidly using automated techniques, providing an additional tool for researchers exploring fatherhood.

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