scholarly journals Movie Recommendation Model from Data through Online Streaming

2021 ◽  
Vol 9 (8) ◽  
pp. 1549-1551
Author(s):  
Runali Komurlekar
Keyword(s):  
Recommendation System ◽  
Rating Scale ◽  
Training Model ◽  
User Preference ◽  
Consumption Pattern ◽  
User Similarity ◽  
Average Rating ◽  
System User ◽  
Movie Recommendation ◽  
The Right

Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern

Design and evaluation of a movie recommendation system showing a review for evoking interested

2017 ◽  
Vol 13 (1) ◽  
pp. 72-84 ◽  
Author(s):  
Yuto Ishida ◽  
Takahiro Uchiya ◽  
Ichi Takumi
Keyword(s):  
Recommendation System ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Item Selection ◽  
Simple Method ◽  
Content Type ◽  
Goods And Services ◽  
Outcomes Evaluation ◽  
Movie Recommendation

Purpose In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference. Design/methodology/approach The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations. Findings Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user. Originality/value This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.

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.

Effective user preference mining-based personalised movie recommendation system

2020 ◽  
Vol 13 (3) ◽  
pp. 371
Author(s):  
Marimuthu Karuppiah ◽  
Hamid Reza Karimi ◽  
D. Malathi ◽  
V. Vijayakumar ◽  
R. Logesh ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Preference ◽  
Movie Recommendation

Effective user preference mining-based personalised movie recommendation system

2020 ◽  
Vol 13 (3) ◽  
pp. 371
Author(s):  
V. Subramaniyaswamy ◽  
R. Logesh ◽  
D. Malathi ◽  
V. Vijayakumar ◽  
Hamid Reza Karimi ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Preference ◽  
Movie Recommendation

scholarly journals On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2138
Author(s):  
Sang-Min Choi ◽  
Dongwoo Lee ◽  
Chihyun Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Magic Number ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Minimum Number ◽  
Movie Recommendation ◽  
Content Based Filtering

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

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.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Correlation analysis of the x-ray and functional parameters from the results of chevron (Austin) osteotomy for hallux valgus

2020 ◽  
pp. 54-63
Author(s):  
Ruslan Khairutdinov ◽  
Timur Minasov ◽  
Ekaterina Yakupova ◽  
Elvina Mukhametzyanova
Keyword(s):  
Hallux Valgus ◽  
Rating Scale ◽  
Metatarsophalangeal Joint ◽  
Valgus Deformity ◽  
First Metatarsophalangeal Joint ◽  
Chevron Osteotomy ◽  
Hallux Valgus Deformity ◽  
Valgus Angle ◽  
The Right ◽  
Bone Structures

Hallux valgus is characterized by the appearance and growth of a painful “lump” in the region of the first metatarsophalangeal joint, the development of forefoot corns, and inability to choose the right shoes, which leads to a significant decrease in the quality of life of these patients. Corrective osteotomies that preserve the metatarsophalangeal joint, for example Austin (Chevron) osteotomy, are usually used for hallux valgus deformity of the I, II degrees. Radiography with the study of the hallux valgus angle (HVA), the intermetatarsal angle (IMA), the distal metatarsal articular angle (DMAA) is a research method that shows the true correlation between bone structures. The correlation between the radiological and functional indicators of osteotomy allows us to determine possible recommendations for indications for surgical treatment of Hallux valgus. Correlation shows that the largest correction of hallux valgus in older patients occurs due to a small adjustment of the angle of DMMA and HVA. IMA had the best correction after Austin osteotomy among patients of a younger age, then the HVA, and the DMMA had minimum correction according to the AOFAS rating scale (Kitaoka). The revealed correlations allow us to determine the correct tactics for the treatment of hallux valgus by identifying the benefits of Austin osteotomy.

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