Machine Learning Based Movie Recommendation System

2021 ◽  
Author(s):  
Nitasha Soni ◽  
Krishan Kumar ◽  
Ashish Sharma ◽  
Satyam Kukreja ◽  
Aman Yadav
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Movie Recommendation

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 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 Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4925-4929
Author(s):  
Shobana M
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Social Life ◽  
Recommendation System ◽  
Information Filtering ◽  
Large Database ◽  
Effective Manner ◽  
Movie Recommendation ◽  
Commercial Applications

A movie recommendation is important in our social life due to its strength in providing enhanced entertainment. Such a system can suggest a set of movies to users based on their interest, or the popularities of the movies. A recommendation system is used for the purpose of suggesting items to purchase or to see. They direct users towards those items which can meet their needs through cutting down large database of Information. A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing

scholarly journals Movie Recommendation Based on Posters and Still Frames using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1747-1750
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recommendation System ◽  
Data Augmentation ◽  
Convolution Neural Network ◽  
Lasso Regression ◽  
Network Concept ◽  
Movie Recommendation ◽  
Fully Connected ◽  
Filter Layer

Movie recommendation system has become a key part in online movie services to gain and maintain the huge market. While within the preceding studies works Convolution neural network (CNN) concept is employed to spot the various movies with similar posters or stills to recommend the users. Using CNN, similar posters and stills are classified into group and any hard cash within the poster may place it out of the group. But the CNN method isn't fully connected and uses backpropagation technique which could be a touch slow within the poster identification and more over just with posters the films cannot be of comparable one and should disappoint the user. Technologies like Fully Convoluted neural network (FCN) makes use of Convolution neural network concept by connecting all neural networks and adding filters and pooling layer in between each filter layer. Data Augmentation is an algorithm which helps in increasing accuracy for the predicting movies. LASSO regression is employed to get images of high multicollinearity. Soft-max layer is employed to work out the probability of the similarities int poster to create it more appropriate for the user. K-means clustering is employed to classify the films still further to recommend thes implest movietotheuser

scholarly journals Machine Learning Model for Movie Recommendation System

2020 ◽  
Vol V9 (04) ◽  
Author(s):  
M. Chenna Keshava ◽  
P. Narendra Reddy ◽  
S. Srinivasulu ◽  
B. Dinesh Naik ◽  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Model ◽  
Machine Learning Model ◽  
Movie Recommendation

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.

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