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Relevant bibliographies by topics / Movie Recommendation System

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Journal articles

Academic literature on the topic 'Movie Recommendation System'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "Movie Recommendation System"

1

Shishodia, Dinesh. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4919–24. http://dx.doi.org/10.22214/ijraset.2021.35929.

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This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.

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2

B, Adithya. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (2020): 120–22. http://dx.doi.org/10.22214/ijraset.2020.32064.

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., Darshini M., Abishay Raina ., Rakshit Mysore Lokesh ., Mohammed Noorulla Khan Durrani ., and T. H. Sreenivas . "MOVIE RECOMMENDATION SYSTEM." International Journal of Engineering Applied Sciences and Technology 03, no. 11 (2019): 39–41. http://dx.doi.org/10.33564/ijeast.2019.v03i11.008.

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4

Raj, Kunal, Atulya Abhinav Das, Antariksh Guha, Parth Sharma, and Mohana Kumar S. "Movie Recommendation System." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1024–28. http://dx.doi.org/10.26438/ijcse/v7i4.10241028.

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5

Li, Bo, Yibin Liao, and Zheng Qin. "Precomputed Clustering for Movie Recommendation System in Real Time." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/742341.

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A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.

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Manavi, Vallari, Anjali Diwate, Priyanka Korade, and Anita Senathi. "MoView Engine : An Open Source Movie Recommender." ITM Web of Conferences 32 (2020): 03008. http://dx.doi.org/10.1051/itmconf/20203203008.

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Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.

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7

Verma, Rupal. "Movie Recommendation System by Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 888–92. http://dx.doi.org/10.22214/ijraset.2021.38084.

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Abstract: This is the era of modern technology where we are all surrounded and covered by technology. This eases our daily life and saves our time and one of the most important techniques that played a very important role in our day-to-day life is the recommendation system. The recommendation system is used in various fields like it is used to recommend products, books, videos, movies, news, and many more. In this paper, we use a Recommendation system for movies we built or a movie recommendation system. It is based on a collaborative filtering approach that makes use of the information provided by the users, analyzes them and recommends movies according to the taste of users. The recommended movie list sorted according to the ratings given to this system is developed in python by using pycharm IDE and MYSQL for database connectivity. The presented recommendation system generates recommendations using various types of knowledge and data about users. Our Recommendation system recommends movies to each and every user by their previous searching history. Here we use some searching techniques as well. We also tried to overcome the cold start problem we use Movielens database. Keywords: Collaborative-filtering, Content-based filtering, Clustering, Recommendation system searching technique, Movies

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Manjeet Singh and Namita Goyal. "Collaborative Filtering Movie Recommendation System." International Journal for Modern Trends in Science and Technology 6, no. 12 (2021): 471–73. http://dx.doi.org/10.46501/ijmtst061291.

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Recommendation System plays an important role in today’s era of e-commerce. From OTT platforms to the shopping application and music application everywhere we see that after watching a movie or buying an item, listening a song we are recommended with some other movie or item or song. Most of the time we select our next movie, item or song from the recommended one. In this paper I will give you a brief description of collaborative and user-based filtering. The data used in this research is taken from Movie Lens. The result obtained contains some movie recommendations.

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9

M, Shobana. "Movie Recommendation System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4925–29. http://dx.doi.org/10.22214/ijraset.2021.35990.

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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

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10

Deshmukh, Puja, and Geetanjali Kale. "Music and Movie Recommendation System." International Journal of Engineering Trends and Technology 61, no. 3 (2018): 178–81. http://dx.doi.org/10.14445/22315381/ijett-v61p229.

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