Movie Recommendation System Using Genome Tags and Content-Based Filtering

2018 ◽  
pp. 85-94 ◽  
Author(s):  
Syed M. Ali ◽  
Gopal K. Nayak ◽  
Rakesh K. Lenka ◽  
Rabindra K. Barik
Keyword(s):  
Recommendation System ◽  
Movie Recommendation ◽  
Content Based Filtering

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 Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

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 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.

A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment

2017 ◽  
Vol 9 (1) ◽  
pp. 25-36 ◽  
Author(s):  
Jaime Raigoza ◽  
Vikrantsinh Karande
Keyword(s):  
High Performance ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Software As A Service ◽  
Psychological Profile ◽  
Choice Overload ◽  
Data Set ◽  
Movie Recommendation ◽  
Movie Ratings ◽  
Content Based Filtering

The availability of huge amounts of data in recent years have led users to being faced with an overload of choices. The outcome is a growth on the importance of recommendation systems due to their ability to solve this choice overload problem, by providing users with the most relevant products from many possible choices. For producing recommendations, things like a user's psychological profile, their browsing history and movie ratings from other users can be considered. To determine how strongly two user's behavior are related to each other, a Pearson correlation coefficient value is often calculated. In this paper, we study the recommendation system on a proposed cloud based environment to produce a list of recommended movies based on a user's profile information. Based on the Software-as-a-Service (SaaS) model implemented, we discuss the concepts such as collaborative filtering and content-based filtering. Given a MovieLens data-set, our results indicate that the proposed approach can provide a high performance in terms of precision, and generate more reliable and personalized movie recommendations, when given a greater number of movies rated by a user. An evaluation was done under minimal known data, which commonly leads to the cold-start problem.

scholarly journals Movie Recommendation System by Using Collaborative Filtering

2021 ◽  
Vol 9 (9) ◽  
pp. 888-892
Author(s):  
Rupal Verma
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Daily Life ◽  
Modern Technology ◽  
Types Of Knowledge ◽  
Movie Recommendation ◽  
Database Connectivity ◽  
Content Based Filtering ◽  
Cold Start Problem ◽  
Filtering Approach

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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