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 COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

scholarly journals Comparative Study of Machine Learning Algorithms for Recommendation System

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Sonam Singh ◽  
◽  
Kriti Srivastva ◽  
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Research Work ◽  
Previous History ◽  
Machine Learning Algorithms ◽  
The Given ◽  
User History

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

scholarly journals Development of machine learning algorithms for a recommendation system for selecting musical compositions

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
D. D. Demydov ◽  
◽  
I. S. Shcherbyna ◽  
N. A. Trintina ◽  
A. M. Shtimmerman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Main Tool ◽  
Machine Learning Algorithms ◽  
Separate Analysis ◽  
Data Set ◽  
Economic Point ◽  
Musical Compositions

The article analyzes the method of singular value decomposition (SVD) as an effective way to build a recommender system. With the development of information technologies and their introduction into public life, there is a need to search for accentuated information in conditions of uncertainty. To solve such problems, recently created intelligent recommendation systems [1]. The popularity of recommendation systems is growing in every segment of goods and services, in particular music. From a socio-economic point of view, such systems are the main tool for the dissemination of new compositions in the field of music promotes the promotion of these compositions in accordance with the preferences of the target audience and encourages users to purchase new music tracks. In addition, such systems significantly reduce the time and facilitate the search for appropriate musical compositions under conditions of uncertainty. The main problem in developing machine learning algorithms is the lack of an individual approach to each user. All recommendations are based on the statistical behavior of the majority, resulting in a percentage of people who do not receive recommendations that match their personal preferences. In the case of a separate analysis of each of the users and the implementation of recommendations in accordance with their personal use of Internet resources, the number of quality and more accurate proposals in the list of recommendations would increase significantly. Machine learning methods are effectively used to build recommendation systems, namely: the k-nearest neighbors method, the Bayesian algorithm and the singular matrix decomposition method. Among these methods, the SVD method is the most widely used in practice. This method is used to reduce the number of non-significant data set factors. Factors in recommendation systems are properties that describe the user or subject. In music recommendation systems, this can be a genre. SVD reduces the dimension of the matrix by removing its hidden factors.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals Movie Recommender System Using Machine Learning Algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012052
Author(s):  
Piyush Kumar ◽  
Shaik Golam Kibriya ◽  
Yuva Ajay ◽  
Ilampiray
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

Machine learning algorithms for smart data analysis in the Internet of things: an overview

2021 ◽  
pp. 307-327
Author(s):  
Mohammed H. Alsharif ◽  
Anabi Hilary Kelechi ◽  
Imran Khan ◽  
Mahmoud A. Albreem ◽  
Abu Jahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Smart Data ◽  
The Internet Of Things

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals Machine Learning as a method of adapting offers to the clients

2019 ◽  
Vol 13 ◽  
pp. 267-271
Author(s):  
Jacek Bielecki ◽  
Oskar Ceglarski ◽  
Maria Skublewska-Paszkowska
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Filtering Method ◽  
Advantages And Disadvantages ◽  
Information Filter ◽  
User Ratings ◽  
Research Show

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

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